ARTICLE | doi:10.20944/preprints201808.0507.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: hydrologic forecast verification, mean squared forecast error, methods of forecast error estimation, comparison of hydrologic forecasting methods, forecast applicability assessment.
Online: 29 August 2018 (16:11:24 CEST)
This paper presents the methods of estimating the mean square error of hydrological forecasts, allowing for assessment of their practical applicability. Depending upon the amount and composition of available hydrometeorological data, an appropriate method for forecast error estimation is chosen. A system of statistical tests for comparison of different forecasting methods for the same hydrologic characteristic with the same lead time is presented. These tests allow for choosing an optimal and most accurate forecasting method. Hydrological forecasting method efficiency estimation is based on comparing the forecast error with climatology or inertial (persistence) forecast error using presented tests.
ARTICLE | doi:10.20944/preprints202110.0037.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: photovoltaic generation forecast; probabilistic forecast; prediction interval; ensemble forecast; day ahead forecasting; multiple PV forecasting
Online: 4 October 2021 (09:55:37 CEST)
Photovoltaic (PV) generation is potentially uncertain. Probabilistic PV generation forecasting methods have been proposed with prediction intervals (PIs). However, several studies have dealt with geographically distributed PVs in a certain area. In this study, a two-step probabilistic forecast scheme is proposed for geographically distributed PV generation forecasting. Each step of the proposed scheme adopts ensemble forecasting based on three different machine-learning methods. In this case study, the proposed scheme was compared with conventional non-multistep forecasting. The proposed scheme improved the reliability of the PIs and deterministic PV forecasting results through 30 days of continuous operation with real data in Japan.
ARTICLE | doi:10.20944/preprints202206.0428.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: machine learning; weather forecast
Online: 30 June 2022 (11:30:04 CEST)
Weather forecast has a big impact on the global economy, accurate and timely weather forecast is required by all, it affects many aspects of human livelihood and lifestyle, it also plays a critical role in decision making for severe weather management and for primary and secondary sectors like agriculture, transportation, tourism, and industry as they rely on good weather conditions for production and operations. The erratic and uncertain complex nature of the weather makes traditional weather forecasting tedious and a challenging task, traditional weather forecast involves applying technology and scientific knowledge on numerical weather prediction (NWP), and weather radar to solve complex mathematical equations to obtain forecasts based on current weather conditions. These traditional processes utilize expensive, complex physical and computational power to produce forecasts, which can be inaccurate and have various catastrophic impacts on society. In this research, a machine learning-based weather forecasting model was proposed, the model was implemented using 4 classifier algorithms which include Random Forest classifier, Decision Tree Algorithm, Gaussian Naïve Bayes model, and Gradient Boosting Classifier, these algorithms were trained using a publicly available dataset from Kaggle for the city of Seattle for the period 2012 to 2015. The model’s performance was evaluated; the Gaussian Naive Bayes algorithm proved to be the best performing algorithm with a predictive accuracy of 84.153 %.
ARTICLE | doi:10.20944/preprints202311.1538.v1
Subject: Environmental And Earth Sciences, Sustainable Science And Technology Keywords: PV power forecasting; deterministic forecast; machine learning; deep learning; satellite forecast, ensemble models; solar; clear sky index, short-term forecast, NWP
Online: 23 November 2023 (13:39:39 CET)
Accurate short-term solar irradiance forecasting is crucial for the efficient operation of solar energy driven photovoltaic (PV) power plants. In this research, we introduce a novel hybrid ensemble forecasting model that amalgamates the strengths of machine learning tree-based models and deep learning neuron-based models. The hybrid ensemble model integrates the interpretability of tree-based models with the capacity of neuron-based models to capture complex temporal dependencies within solar irradiance data. Furthermore, stacking and voting ensemble strategies are employed to harness the collective strengths of these models, significantly enhancing prediction accuracy. This integrated methodology is enhanced by incorporating pixels from satellite images provided by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). These pixels are converted into structured data arrays and employed as exogenous inputs in the algorithm. The primary objective of this study is to improve the accuracy of short-term solar irradiance predictions, spanning a forecast horizon up to 6 hours ahead. The incorporation of EUMETSAT satellite image pixels data enables the model to extract valuable spatial and temporal information, thus enhancing the overall forecasting precision. This research also includes detailed analysis of the derivation of GHI using satellite images. The study was carried out and the models tested across three distinct locations in Austria. A detailed comparative analysis was carried out for traditional Satellite (SAT) and Numerical Weather Prediction (NWP) models with hybrid models. Our findings demonstrate a higher skill score for all of the approaches compared to smart persistent model and consistently highlight the superiority of the hybrid ensemble model for short-term prediction window of 1 to 6 hours. This research underscores the potential for enhanced accuracy of the hybrid approach to advance short-term solar irradiance forecasting, emphasizing its effectiveness at understanding the intricate interplay of the meteorological variables affecting solar energy generation worldwide. The results of this investigation carry noteworthy implications for advancing solar energy systems, thereby supporting the sustainable integration of renewable energy sources into the electrical grid.
ARTICLE | doi:10.20944/preprints202002.0217.v2
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: cost-loss; forecast change; forecast volatility; decision making; expected utility; probabilistic forecasts; ensemble forecasts
Online: 8 May 2020 (04:28:30 CEST)
Users of meteorological forecasts are often faced with the question of whether to make a decision now based on the current forecast or whether to wait for the next and hopefully more accurate forecast before making the decision. One would imagine that the answer to this question should depend on the extent to which there is a benefit in making the decision now rather than later, combined with an understanding of how the skill of the forecast improves, and information about the possible size and nature of forecast changes. We extend the well-known cost-loss model for forecast-based decision making to capture an idealized version of this situation. We find that within this extended cost-loss model, the question of whether to decide now or wait depends on two specific aspects of the forecast, both of which involve probabilities of probabilities. For the special case of weather and climate forecasts in the form of normal distributions we derive a simulation algorithm, and equivalent analytical expressions, for calculating these two probabilities. We apply the algorithm to forecasts of temperature and find that the algorithm leads to better decisions relative to three simpler alternative decision-making schemes. Similar problems have been studied in many other fields, and we explore some of the connections.
ARTICLE | doi:10.20944/preprints202308.2010.v1
Subject: Environmental And Earth Sciences, Sustainable Science And Technology Keywords: SPI; SPEI; CSIC; CMIP6 ssp126; MK Test; Amman Zarqa Basin-Jordan; drought forecast; forecast models
Online: 30 August 2023 (08:10:45 CEST)
Different drought indices are used to quantify its characteristics. This research applied many approaches to assessing the uncertain SPI and SPEI and the most capturing index of drought. Machine learning algorithms are used to predict drought; TBATS and ARIMA models run diverse input sources including observations, CSIC, and CMIP6-ssp126 datasets. The longest drought duration was 14 months. Drought severity and average intensity were found -24.64 and -1.76, -23.80 and -1.83, -23.57 and -1.96, -23.44 and -2.0 where the corresponding drought categories were SPI 12 -Sweileh, SPI 9 Sweileh, SPI 12 Wadi Dhullail, SPI 12 Amman-Airport. The dominant drought incident occurred between Oct 2020 and Dec 2021. CMIP6 can capture the drought occurrence and severity by measuring SPI but did not capture the severity magnitude same as from observations (-2.87 by observation and -1.77 by CMIP6). Using observed SPI and historical CMIP6, ARIMA was the most accurate than TBATS. Regarding SPEI forecast, ARIMA was the most accurate model to forecast drought index using the observed historical SPEI and CSIC over all stations. The performance metrics ME, RMSE, MAE, and MASE implied significantly promising forecasting models; -0.0046, 0.278, 0.179, & 0.193 respectively for ARIMA and -0.0181, 0.538, 0.416, & 0.466 respectively for TBATS. Hybrid modelling is suggested for more consistency and robustness of forecasting approaches.
ARTICLE | doi:10.20944/preprints201809.0089.v1
Subject: Engineering, Energy And Fuel Technology Keywords: hybrid forecast model; forecast horizon; daily global solar radiation clustering; fuzzy c-means; variability characterization
Online: 5 September 2018 (06:22:35 CEST)
In this paper, the forecast horizon and solar variability influences on MHFM model based on multiscale decomposition, AR and NN models, are studied. This article follows the works published in  showing the performance of the MHFM using 3 multiscale decomposition methods and a forecast horizon equal to 1 hour. Several forecast horizon strategies and his influence on the MHFM performances are investigated. We show that the best strategy for a rRMSE variying from $4.43\%$ to $10.24\%$ is obtained for forecast horizons from $5$ minutes to $6$ hours. In a second part, the solar variability influence on the MHFM is studied. A classification based on a shows that the best performance of MHFM is obtained for clear sky days with a rRMSE of $2.91\%$ and worst for cloudy sky days with a rRMSE of $6.73\%$.
CASE REPORT | doi:10.20944/preprints202102.0214.v1
Subject: Engineering, Automotive Engineering Keywords: bridge; condition; flag; forecast; management; sustainability
Online: 8 February 2021 (15:38:29 CET)
New York State Department of Transportation designates potentially hazardous conditions on bridges as flags. From 1982 until 2006 the flags issued for the bridges owned by New York City underwent all phases typical of crises, including a gradual increase, an exponential expansion, an extended peak, a gradual decline, and a convergence to a higher but manageable level. The attempts to forecast the flag pattern as it was developing are reviewed for possible relevance to management of the transportation infrastructure and in general.
ARTICLE | doi:10.20944/preprints202006.0162.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: COVID 19; forecast; NO2; O3; SO2
Online: 14 June 2020 (03:43:42 CEST)
COVID 19 has caused social distancing and lead to the reductions of various anthropogenic activities. Correspondingly this study has two fold objectives. First, aims to provide quantification measurement of social distancing impacts on air quality. Second, to forecast the air quality if social distancing is continued. The measured air quality parameters consist of NO2, SO2, and O3. According to the results, the order of air quality parameters was NO2<SO2<O3. The NO2, SO2, and O3 levels were observed lower after social distancing than before social distancing was implemented. The reductions of NO2, SO2, and O3 levels were 5%, 3%, and 5% respectively. Likewise, 65% of study periods (30 days) after implementation of social distancing have lower NO2 than before social distancing. The exponential smoothing forecasts show the decreasing trends for NO2 and SO2. While O3 levels are estimated will remain stable after social distancing. This study has shown that the social distancing has an impact on the NO2, SO2, and O3. Correspondingly, if the social distancing is continued, then it is estimated can provide a positive impact on urban quality. Keywords: COVID 19, forecast, NO2, O3, SO2.
ARTICLE | doi:10.20944/preprints202302.0243.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: ensemble; wind forecast; dynamical downscaling; breeding; blending
Online: 14 February 2023 (09:36:21 CET)
This work compared the performance of three methods for constructing a regional ensemble prediction system (EPS) for wind speed forecasts: dynamical downscaling, breeding of growth modes (BGM), and blending method. The Weather Research and Forecasting (WRF) model was used to downscale the European Centre for Medium-range Weather Forecast (ECMWF) EPS. In addition, as the BGM method needs observation data for generating scaling factors, an alternative method for generating scaling factors was proposed to eliminate dependence on observation data. One-month tests between October 1st and October 30th, 2020, were implemented to evaluate the performance of three methods in the Gansu province of China. The results demonstrate that the blending method outperforms the other two methods. Furthermore, the difference in performance is evident mainly in early forecast lead time and becomes negligible as forecast time increases.
ARTICLE | doi:10.20944/preprints202007.0128.v1
Subject: Engineering, Energy And Fuel Technology Keywords: photovoltaic power forecast; energy markets; solar imbalance
Online: 7 July 2020 (16:23:08 CEST)
One of the major problem of photovoltaic grid integration is limiting the solar-induced imbalances since these can undermine the security and stability of the electrical system. Improving the forecast accuracy of photovoltaic generation is becoming essential to allow a massive solar penetration. In particular, improving the forecast accuracy of large solar farms generation is important both for the producers/traders to minimize the imbalance costs and for the Transmission System Operators to insure stability. In this article, we provide a benchmark for the day-ahead forecast accuracy of utility scale PV plants in 1325 locations spanning the country of Italy. We then use these benchmarked forecasts and real energy prices to compute the economic value of forecast accuracy and accuracy improvement in the context of the Italian energy market regulatory framework. Through this study, we further point out some several important criticisms of the Italian “single pricing” system that brings to paradoxical and counterproductive effects regarding the need to reduce the imbalance volumes. Finally, we propose a new market-pricing rule and innovative actions to overcome these undesired effects of the current dispatching regulations.
ARTICLE | doi:10.20944/preprints202007.0065.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: NDVI; EVI; Wheat; Yield forecast; Landsat 8
Online: 5 July 2020 (11:14:40 CEST)
Due to increase demand of food grain in the world, assessment of yield before actual production is important in making policies and decisions in agricultural production system. For a large area, forecast models developed from vegetation indices derived from remote sensing satellite data possesses the potential to give quantitative and timely information on crops over large areas. Different vegetation indices are being made used for this purpose, however, their efficiency in estimating crop yield is needed to be certainly tested. In this study, wheat yield forecast was derived by regressing ground truthing yield data against time series of spatial vegetation indices for the 2013 to 2019 growing seasons. These spatial vegetation indices derived from Landsat 8 image data: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were compared to evaluate the most appropriate index that performs better in forecasting wheat production at Karcag, Kunhegyes and Ecsegfalva settlements in Jász-Nagykun-Szolnok county, in the Northern Great Plain region of central Hungary. The best time for making wheat yield prediction with Landsat 8- SAVI and NDVI was found to be the beginning of ripening period (160th day of the year) with higher correlation between the vegetation indices and the wheat yield. The validation results revealed that the model from SAVI provides more consistent and accurate forecasts yield compared to NDVI. The SAVI model forecast yield for the validation years, 2018 and 2019 were within 6.00% and 4.41% of the final reported values while that of NDVI model were within 8.31% and 6.27%. Nash-Sutcliffe efficiency index is positive with E1= 0.99 for the model from SAVI and for NDVI, E1=0.57, which connote that the forecasting method developed and evaluated performs acceptable forecast efficiency.
ARTICLE | doi:10.20944/preprints202004.0313.v1
Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: covid-19; forecast; epidemic; logistic model; pandemic
Online: 19 April 2020 (02:21:53 CEST)
This note applies the Logistic model approximation to determine the suitable start and end dates for the observed epidemic curves in the total number of cases for different countries. The Logistic model is presented and explicit relations for the beginning and end dates are obtained together with the total epidemic duration. Using data from Brazil, Germany, Italy, and South Korea, the extreme dates are calculated. Since the epidemic onset time is found, a fair comparison of the epidemic curve for these countries is obtained. The result does not depend on the poor statistics available in the early phase of the epidemic when the initial number of infectives is unknown. In fact, the total duration depends only on the characteristic time parameter of the LM model.
ARTICLE | doi:10.20944/preprints202004.0049.v1
Subject: Medicine And Pharmacology, Epidemiology And Infectious Diseases Keywords: Covid-19; epidemic in Italy; statistical forecast
Online: 6 April 2020 (11:28:31 CEST)
We statistically investigate the Coronavirus Disease 19 (hereinafter Covid-19) epidemics, which is particularly invasive in Italy. We show that the high apparent mortality (or Case Fatality Ratio, CFR) observed in Italy, as compared with other countries, is likely biased by a strong underestimation of infected cases. To give a more realistic estimate of the mortality of Covid-19, we use the most recent estimates of the IFR (Infection Fatality Ratio) of epidemic, based on the minimum observed CFR, and furthermore analyse data obtained from the ship Diamond Princess, a good representation of a ‘laboratory’ case-study from an isolated system in which all the people have been tested. From such analyses we derive more realistic estimates of the real extension of the infection, as well as more accurate indicators of how fast the infection propagates. We then point out from the various explanations proposed, the dominant factors causing such an abnormal seriousness of the disease in Italy. Finally, we use the deceased data, the only ones estimated to be reliable enough, to predict the total number of infected people and the interval of time when the infection in Italy could stop.
ARTICLE | doi:10.20944/preprints201904.0058.v1
Subject: Business, Economics And Management, Econometrics And Statistics Keywords: load forecast; short term; probabilistic; Gaussian processes
Online: 4 April 2019 (16:01:54 CEST)
We provide a comprehensive framework for forecasting five minute load using Gaussian processes with a positive definite kernel specifically designed for load forecasts. Gaussian processes are probabilistic, enabling us to draw samples from a posterior distribution and provide rigorous uncertainty estimates to complement the point forecast, an important benefit for forecast consumers. As part of the modeling process, we discuss various methods for dimension reduction and explore their use in effectively incorporating weather data to the load forecast. We provide guidance for every step of the modeling process, from model construction through optimization and model combination. We provide results on data from the PJMISO for various periods in 2018. The process is transparent, mathematically motivated, and reproducible. The resulting model provides a probability density of five-minute forecasts for 24 hours.
ARTICLE | doi:10.20944/preprints202105.0261.v1
Subject: Computer Science And Mathematics, Probability And Statistics Keywords: forecasting; forecast evaluation; forecast bias; mean bias; median bias; MPE; AvgRel-metrics; AvgRelAME; AvgRelAMdE; RelAME; RelMdE; AvgRelME; AvgRelMdE; OPc
Online: 12 May 2021 (09:48:29 CEST)
Measuring bias is important as it helps identify flaws in quantitative forecasting methods or judgmental forecasts. It can, therefore, potentially help improve forecasts. Despite this, bias tends to be under-represented in the literature: many studies focus solely on measuring accuracy. Methods for assessing bias in single series are relatively well-known and well-researched, but for datasets containing thousands of observations for multiple series, the methodology for measuring and reporting bias is less obvious. We compare alternative approaches against a number of criteria when rolling-origin point forecasts are available for different forecasting methods and for multiple horizons over multiple series. We focus on relatively simple, yet interpretable and easy-to-implement metrics and visualization tools that are likely to be applicable in practice. To study the statistical properties of alternative measures we use theoretical concepts and simulation experiments based on artificial data with predetermined features. We describe the difference between mean and median bias, describe the connection between metrics for accuracy and bias, provide suitable bias measures depending on the loss function used to optimise forecasts, and suggest which measures for accuracy should be used to accompany bias indicators. We propose several new measures and provide our recommendations on how to evaluate forecast bias across multiple series.
ARTICLE | doi:10.20944/preprints202305.1373.v1
Subject: Engineering, Civil Engineering Keywords: monthly precipitation forecast; wavelet-based machine learning; teleconnections
Online: 19 May 2023 (04:12:22 CEST)
An accurate and timely precipitation forecast is essential for water resources management in hydropower, irrigation, and reservoir control. The conventional methods are limited by their inability to capture the high precipitation variability in time and space. In the present work, a wavelet-based deep learning approach is adopted to forecast precipitation using the lagged monthly rainfall, local climate variables, and global teleconnections such as IOD, PDO, NAO, and Nino 3.4 as predictors. The method was tested and validated over the Krishna River Basin in India. Overall, the forecasting accuracy was higher using the wavelet-based hybrid models than the single-scale models. The proposed multi-scale model was then applied to the different climatic regions of the country, and it was shown that the model could forecast the rainfall at reasonable accuracy for different climate zones of the country.
COMMUNICATION | doi:10.20944/preprints202206.0137.v1
Subject: Medicine And Pharmacology, Pulmonary And Respiratory Medicine Keywords: FAIR; epidemiology; models; pandemic forecast; SIR modelling; standards
Online: 9 June 2022 (07:55:55 CEST)
A major challenge for the dissemination, replication, and reuse of epidemiological forecasting studies during COVID-19 pandemics is the lack of clear guidelines and platforms to exchange models in a Findable, Accessible, Interoperable, and Reusable (FAIR) manner, facilitating reproducibility of research outcomes. During the beginning of pandemics, models were developed in diverse tools that were not interoperable, opaque without traceability and semantics, and scattered across various platforms - making them hard to locate, infer and reuse. In this work, we demonstrate that implementing the standards developed by the systems biology community to encode and share COVID-19 epidemiological models can serve as a roadmap to implement models as a tool in medical informatics, in general. As a proof-of-concept, we encoded and shared 24 epidemiological models using the standard format for model exchange in systems biology, annotated them with cross-references to data resources, packed up all associated files in COMBINE archives for easy sharing, and finally, disseminated the models through BioModels repository to significantly enhance their reproducibility and repurposing potential. We recommend the use of systems biology standards to encode and share models of epidemic and pandemic forecasts to improve their findability, accessibility, interoperability, reusability, and reproducibility.
ARTICLE | doi:10.20944/preprints202005.0473.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: ARIMA; COVID 19; forecast; PM2.5; PM10; social distancing
Online: 31 May 2020 (15:32:49 CEST)
It has been hypothesized that social distancing as the prevention measures for COVID 19 can affect the air quality including PM2.5 and PM10 in urban areas. According to this situation, this study aims to compare the PM2.5 and PM10 before and after the implementation of social distancing. Likewise, this study also forecasts the benefits of social distancing on PM2.5 and PM10 if social distancing period is continued and extended. To achieve these objectives, an Auto Regressive Integrated Moving Average (ARIMA) model to investigate the daily PM2.5 and PM10 trends has been developed for social distancing periods (March– May 2020) and after May as well. The model confirms that if social distancing period is extended after May 2020 then the PM2.5 and PM10 are estimated will be 4% and 9% lower. To confirm that the PM2.5 and PM10 reductions are only due to social distancing effect, the study has investigated the possible effects of wind speed and rainfall on PM2.5 and PM10. Nonetheless, the reductions do not correlate with those factors. To conclude social distancing should be considered as an option to control PM2.5 and PM10 in urban areas.
ARTICLE | doi:10.20944/preprints202206.0151.v1
Subject: Engineering, Control And Systems Engineering Keywords: Forecast splashing; suppression splashing; AOD; signal fusion; fuzzy control
Online: 10 June 2022 (07:51:53 CEST)
During the smelting process of AOD furnace, the unbalanced reaction of material will lead to the occurrence of splashing. It will not only damage the smelting equipment, but also seriously injure the personnel. In this study, first, the information of liquid level, audio information, and vibration information are detected by multiple sensors respectively. Then, the fused information is used to forecast the splashing. Finally, the multitasking fuzzy controller is used to suppress splashing. The results show that the method of forecasting and suppressing splashing can accurately forecast and achieve rapid suppression. Thus, the efficiency of smelting can be improved.
ARTICLE | doi:10.20944/preprints202104.0389.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: Trends in meteorological data; SWAT, RCP; Mann-Kendall forecast
Online: 14 April 2021 (16:06:14 CEST)
In this study, we have first studied the trend in meteorological data from the Harmaleh, Vanai and Farsesh stations in the 50-year period in the Dez catchment area. The meteorological data will be then forecasted using SWAT and Mann-Kendall. Forecasting the results in the Mann-Kendall and SWAT model has been done using the code written in MATLAB software and RCP (4.5, 8.5) scenarios, respectively. Studying the results of the trend in the data of meteorological stations in this catchment area indicated that these two parametric and non-parametric methods have been used to determine trends in meteorological data. The results of the parametric method are positive in all meteorological parameters. Non-parametric method over a period of 50 years shows the presence of trends in the data. The comparison on the forecasting results at maximum temperature suggested that during summer, we will see an increase in temperature compared to the ground state in all three forecasts. The results of the minimum temperature forecast show a decrease in the minimum increase during the winter and the precipitation forecast indicates that at the end of autumn (Nov) precipitation decreased by 20 mm in the Mann-Kendall and 4.5 RCP while RCP8.5 suggests the increase in precipitation compared to the ground state. Studying the runoff forecast results using SWOT show that at the end of winter (Feb) and almost all spring (Mar, Apr) a decrease of about 40%, 15% and 14% will be seen, respectively
ARTICLE | doi:10.20944/preprints202002.0428.v1
Subject: Engineering, Civil Engineering Keywords: small hydropower plant; river flow; seasonal forecast; energy production
Online: 28 February 2020 (12:15:43 CET)
The operation feasibility of small hydropower plants in mountainous sites is subjected to the run-of-river flow which is also depending on a high variability in precipitation and snow cover. Moreover, the management of this kind of systems has to be performed with some particular operation conditions of the plant (e.g. turbine minimum and maximum discharge) but also some environmental flow requirements. In this context, a technological climate service is conceived in tight connection with end users, perfectly answering the needs of the management of small hydropower systems in a pilot area, and providing forecast of river streamflow together with other operation data. This paper presents an overview of the service but also a set of lessons learnt related to features, requirements and considerations to bear in mind from the point of view of climate services developers. In addition, the outcomes give insight into how this kind of services could change the traditional management (normally based on the past experience), providing a probability range of future river flow based on future weather scenarios according to the range of future weather possibilities. This highlights the utility of the co-generation process to implement climate services for water and energy fields but also that seasonal climate forecast could improve the business as usual of this kind of facilities.
ARTICLE | doi:10.20944/preprints202308.0073.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: olive anthracnose; machine learning; forecast models; classification algorithms; soil nutrients
Online: 2 August 2023 (05:36:07 CEST)
Olive Anthracnose (OA) is the most important fungal disease of olive fruits worldwide. In the context of integrated pest management, the development of predictive models could be used for early diagnosis and control. In the current study, a dataset representing 58 cases (6 locations with 12 olive cultivars) was used to study the relationship between ΟΑ incidence (OAI) and 35 heterogeneous variables, including orchard characteristics, olive fruit parameters, foliar and soil nutrients, soil parameters and soil texture classes. The Random Forest-Recursive Feature Elimination with Cross Validation (RF-RFECV) feature selection method identified Location, Water Content, P, Ca, Mg, Exchangeable Mg, Trace Zn, Trace Cu as possible new indicators associated with OAI. Six different classification algorithms, namely Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), were developed for predicting conditions leading to OAI >0% and 10%. Hyperparameter optimization using grid search was used to optimize the parameters of the models and finally the best parameters were applied to predict the OAI. The final models were evaluated in terms of several standard metrics, such as accuracy, sensitivity, specificity and ROC AUC score. Findings suggested that GB performance was superior compared to the other models for the prediction of the occurrence of OA disease (OAI>0%) with an accuracy of 86.7%, a sensitivity of 100%, a specificity of 75% and a ROC-AUC score of 93%, while for the prediction of the spread of the disease (OAI>10%), DT stood out with an accuracy of 86.7%, a sensitivity of 81.8%, a specificity of 100% and a ROC-AUC score of 91%. RF classifier performed very well in both cases, with an accuracy of 80%, a sensitivity of 85.7%, a specificity of 75% and a ROC-AUC score of 93% for the prediction of the occurrence of the disease (OAI>0%), and an accuracy of 86.7%, a sensitivity of 90.9%, a specificity of 75% and a ROC-AUC score of 84% for the prediction of the spread of the disease (OAI>10%).
ARTICLE | doi:10.20944/preprints202307.0988.v1
Subject: Engineering, Energy And Fuel Technology Keywords: load forecast; electrification; heat pumps; electric vehicles; solar; Alaska; Railbelt
Online: 14 July 2023 (09:03:00 CEST)
Load forecasting is an important component of power system and resource planning for electrical grids. The adoption of electric vehicles (EVs), behind-the-meter (BTM) solar, and heat pumps will significant change the amount and variability of loads. Electrification adoption and load forecasting in arctic regions and Alaska is limited. This paper provides the first load and electrification adoption forecast for the Alaska Railbelt transmission system, including yearly adoption rates of EVs, BTM solar, and heat pumps and hourly load data. Adoption rates are based on available historical data and compared to other regional and national trends. Two forecasts are created: 1) a moderate adoption forecast based on projections from current adoption rates and comparisons to other projections, and 2) an aggressive forecast, which provides a bookend comparison at the high adoption rate of 90% for all technologies. The results of these forecasts demonstrate a significant increase in both energy, 80% and 116% for moderate and aggressive, respectively and peak load demand, 113% and 219% for moderate and aggressive, respectively. Additionally, the results indicate a maximum hourly load change of 260% and 381% for the moderate and aggressive forecasts, respectively. These findings highlight a need for resource planning, which accounts for increases in demand and suggests that significant demand management is needed to smooth and control the load fluctuations as a result of the adoption of EVs, BTM solar, and heat pumps.
ARTICLE | doi:10.20944/preprints202207.0383.v1
Subject: Engineering, Marine Engineering Keywords: machine learning; forecast; regression models; Liquified Natural Gas; maritime transportation
Online: 26 July 2022 (03:50:12 CEST)
Recent maritime legislations demand the transformation of the sector to greener and more energy efficient transportation. Liquified Natural Gas (LNG) seems a promising alternative fuel solution that could replace the conventional fuel sources. Various studies have been focused on the prediction of LNG price, however, no previous work has been made on the forecast of spot charter rate of LNG carrier ships. An important knowledge for the maritime industries and companies when it comes to decision-making. Therefore, this study is focused on the development of a machine learning pipeline to address the aforementioned problem by: (i) forming a dataset with variables relevant to LNG; (ii) identifying the variables that impact on the freight price of LNG carrier; (iii) developing and evaluating regression models for short and mid-term forecast. The results showed that the General Regression Neural Network presented a stable overall performance for 2, 4 and 6 months forecast.
ARTICLE | doi:10.20944/preprints202108.0072.v1
Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: IoT; Fungal disease forecast; Botrytis cinerea; Precise agriculture; Decision support
Online: 3 August 2021 (11:20:03 CEST)
This paper presents the evaluation of a fungal disease forecast model in vineyards for qualitative parameter analysis using the data from off the shelf sensors, i.e. temperature and air relative humidity, rain precipitation, and leaf wetness. The rules for the fungal disease models are digitalized as a decision support tool that serve as an indicator to farmers for the need of spraying of the chemical substances to ensure the best growing condition and suppress the level of parasites. The temperature and humidity contexts are used interchangeably in practice to detect the risk of the disease occurrence. By taking into account a number of influences on these parameters collected from the shelf sensors, new topics for research in the multidimensional field of precision agriculture emerge. In this study, the impact of the humidity is evaluated by assessing how different humidity parameters correlate with the accuracy of the Botrytis cinerea fungi forecast. Each humidity parameter has it’s own threshold that triggers the second step of the disease modeling - risk index based on the temperature. The research showed that for humidity a low-cost relative humidity sensor can detect in average 14.61% risk values, a leaf wetness sensor an additional 3.99% risk cases, and finally, a precipitation sensor will detect only an additional 0.59% risk cases.
Subject: Engineering, Automotive Engineering Keywords: Electricity demand forecast; Machine Learning; Artificial Neural Networks; systematic review.
Online: 21 May 2021 (09:48:10 CEST)
The forecast of electricity demand has been a recurrent research topic for decades, due to its economical and strategic relevance. Several Machine Learning (ML) techniques have evolved in parallel with the complexity of the electric grid. This paper reviews a wide selection of approaches that have used Artificial Neural Networks (ANN) to forecast electricity demand, aiming to help newcomers and experienced researchers to appraise the common practices and to detect areas where there is room for improvement in the face of the current widespread deployment of smart meters and sensors, which yields an unprecedented amount of data to work with. The review looks at the specific problems tackled by each one of the selected papers, at the results attained by their algorithms, and at the strategies followed to validate and compare the results. This way, it is possible to highlight some peculiarities and algorithm configurations that seem to consistently outperform others in specific settings.
ARTICLE | doi:10.20944/preprints201912.0007.v1
Subject: Social Sciences, Geography, Planning And Development Keywords: urban; growth model; forecast; built; settlement; machine learning; time series
Online: 2 December 2019 (05:15:03 CET)
Advances in the availability of multitemporal and global built-/human-settlements datasets as derived from Remote Sensing (RS) can now provide globally consistent definitions of “human-settlement” at unprecedented spatial fineness. Yet, these data only provide a time-series of past extents and urban growth/expansion models have not had parallel advances at high-spatial resolution. We present a flexible modelling framework for producing annual built-settlement extents in the near future past last observed extents as provided by RS-based data. Using a random forest and autoregressive temporal models with short time-series of built-settlement extents and subnational level data, we predict annual 100m resolution binary settlement extents five years beyond the last observations. We applied this framework within varying contexts and predicted annual extents from 2010 to 2015. We found that our model framework preformed consistently across all sample countries and, when compared to time-specific imagery, demonstrated the capacity to capture human-settlement missed by the input time-series and validation extents. When comparing building footprints of small settlements to forecast extents, we saw that the modelling framework had a 12 percent increase in ground-truth accuracy. This framework shows promise for predicting near-future settlement extents, and provides a foundation for forecasts further into the future.
ARTICLE | doi:10.20944/preprints201911.0125.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: tropical cyclone; Weather Research and Forecast model; zonal Ekman transport
Online: 12 November 2019 (09:32:21 CET)
We examine the role of zonal Ekman transport along the coast of Senegal on 30 August, 2015 when the tropical disturbance associated with Tropical Cyclone Fred was located to the west of Senegal causing considerable coastal damage to coastal areas south of Dakar, Senegal. Ten-meter winds from three Weather Research and Forecast model simulations were used to estimate zonal Ekman transport, with the largest values found during the 30 August. The simulations are in agreement with limited coastal observations showing increasing southerly wind speeds during 30 August but are overestimated relative to the 3 coastal stations. The strong meridional winds translate into increased zonal Ekman transport to the coast of Senegal on 30 August. The use of a coupled ocean model will improve the estimates of Ekman transport along the Guinea-Senegalese coast. The observed damage suggests that artificial and natural barriers (mangroves) should be strengthened to protect coastal communities in Senegal.
ARTICLE | doi:10.20944/preprints201909.0102.v1
Subject: Business, Economics And Management, Econometrics And Statistics Keywords: ARIMA Methodology; Out-of-Sample Forecast; Tourist Arrivals; Sierra Leone
Online: 9 September 2019 (12:11:03 CEST)
This study have uniquely mad use of Box-Jenkins ARIMA models to address the core of the threes objectives set out in view of the focus to add meaningful value to knowledge exploration. The outcome of the research have testify the achievements of this through successful nine months out-of-sample forecasts produced from the program codes, with indicating best model choices from the empirical estimation. In addition, the results also provide description of risks produced from the uncertainty Fan Chart, which clearly outlined possible downside and upside risks to tourist visitations in the country. In the conclusion, it was suggested that downside risks to the low level tourist arrival can be managed through collaboration between authorities concerned with the management of tourist arrivals in the country.
ARTICLE | doi:10.20944/preprints201810.0593.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: adjusting; classification; ensemble; post-processing; ramp events; solar power forecast
Online: 25 October 2018 (05:43:31 CEST)
In this study an adjusting post-processing approach is implemented for improving intra-hourly forecasts of solar power and ramp events of PV solar power systems at different locations in the United States. This study also serves as an out-of-sample test to evaluate the performance of the adjusting approach with different locations and timescales. Thus, various individual intra-hourly forecasts of solar power are combined and adjusted by applying the adjusting approach. Both point and probabilistic forecasts of solar power are included. After that, solar power ramp event forecasting by the adjusting approach is carried out.
ARTICLE | doi:10.20944/preprints201611.0029.v1
Subject: Environmental And Earth Sciences, Geophysics And Geology Keywords: precipitation deficit; precipitation surplus; standardized precipitation index SPI; forecast; verification
Online: 4 November 2016 (13:39:29 CET)
In the paper the verification of forecasts of precipitation conditions measured by the standardized precipitation index SPI is presented. For the verification of categorical forecasts a contingency table was used. Standard verification measures were used for the SPI value forecast. The 30 day SPI moved every 10 days by 10 days was calculated in 2013-2015 from April to September on the basis of precipitation data from 35 meteorological stations in Poland. Predictions of the 30 day SPI were created in which precipitation was forecasted in the next 10 days (the SPI 10-day forecast) and 20 days (the SPI 20-day forecast). Both for the 10 and 20 days, the forecasts were skewed towards drier categories at the expense of wet categories. There was a good agreement between observed and 10-day forecast categories of precipitation. Less agreement is obtained for 20-day forecasts – these forecasts evidently “over-dry” the assessment of precipitation anomalies. The 10-day SPI value forecast accuracy is acceptable, whereas for the 20-day forecast is unsatisfactory. Both for the SPI categorical and the SPI value forecast, the 10-day SPI forecast is reliable and the 20-day forecast should be accepted with reservation and used with caution.
REVIEW | doi:10.20944/preprints202307.0630.v1
Subject: Engineering, Energy And Fuel Technology Keywords: Review; Machine Learning; Reservoir simulations; History matching; Production optimization; Production forecast
Online: 11 July 2023 (03:13:13 CEST)
In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry with numerous applications which guide engineers in better decision-making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in numerous modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all these applications lead to considerable computational time and computer resources associated costs, rendering reservoir simulators as not fast and robust enough, thus introducing the need for more time-efficient and smart tools, like ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. The first part of the present study (Part I) offers a detailed review of ML techniques in the petroleum industry, specifically in subsurface reservoir simulation, for the cases of individual simulation runs and history matching, whereas the ML-based Production Forecast Optimization applications will be presented in Part II. This review can assist engineers as a complete source for applied ML techniques since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.
ARTICLE | doi:10.20944/preprints202306.1094.v1
Subject: Engineering, Energy And Fuel Technology Keywords: Forecast, Power, Monkeypox, COVID-19, Fuel, Price, Energy, Pandemic, Stochastic, ARIMAX.
Online: 15 June 2023 (07:59:31 CEST)
The COVID-19 epidemic and the measures adopted to contain it have had a significant impact on energy patterns throughout the world. The pandemic and movement restrictions led to unpredictable fluctuations in power systems demand and the fuel price for a delayed period. Monkeypox, another viral disease, appeared during the post-COVID period. It is assumed that the outbreak of monkeypox is unlikely due to the implication of preventive measures experienced by COVID-19. At the same time, the probability of an epidemic cannot be blindly overlooked. This paper aims to examine and analyze historical data to look at how much petroleum fuel was used for generating power and how the price of petroleum fuel changed over seven years, from January 2016 to August 2022. This period covers the time before the COVID-19 pandemic, during the pandemic, and after the pandemic. Several time-series forecasting models, including all four benchmark methods (Mean, Naïve, Drift, and Snaïve), Seasonal and Trend decomposition using Loess (STL), Exponential Smoothing (ETS), and Autoregressive Integrated Moving Average (ARIMA) methods have been applied for both fuel consumption and price prediction. The best forecasting method for fuel price and consumption has been identified among these methods. The paper also utilizes the ARIMAX model by incorporating multiple exogenous variables, such as monthly mean temperature, mean fuel price, and mileage of vehicles traveling during a certain period of pandemic lock-down. It will assist in capturing the non-smooth and stochastic pattern of fuel consumption and price due to the pandemic by separating the seasonal influence and thus provide a prediction of the consumption pattern in the event of any future pandemic.
REVIEW | doi:10.20944/preprints202305.2157.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: Precipitation monitoring; rainfall measurement biases; rain gauge; measurement error; hydrological forecast
Online: 30 May 2023 (13:58:36 CEST)
Tipping bucket rain gauges (TBRs) have been, and apparently will continue to be one of the most widely used pieces of equipment for rainfall monitoring, being frequently used for the calibration, validation and downscaling of radar and remote sensing data, due to their major advantages–low cost, simplicity, and low energy consumption. Thus, many works have focused and continue to focus on their main disadvantage–measurement biases (mainly in wind and mechanical underestimations). However, despite arduous scientific effort, calibration methodologies are not frequently implemented by monitoring networks operators or data users, propagating bias in databases and in the different applications of such data, causing uncertainty in the modeling, management, and forecasting in hydrological research, mainly due to a lack of knowledge. Within this context, this work presents a review of the scientific advances in TBR measurement uncertainties, calibration, and error reduction strategies from a hydrological point of view, by describing different rainfall monitoring techniques in Section 2, summarizing TBR measurement uncertainties in Section 3, focusing on calibration, and error reduction strategies in Section 4, a discussion and perspectives in Section 5, and conclusions in Section 6, providing an overview of the of the state of the art and future perspectives of the technology.
ARTICLE | doi:10.20944/preprints202008.0277.v1
Subject: Computer Science And Mathematics, Information Systems Keywords: Collaborative forecast; Support vector regression; China-Japan-South Korea; Primary energy consumption
Online: 12 August 2020 (08:13:35 CEST)
This study aims at improving the forecast accuracy of primary energy consumptions in China, Japan and South Korea and verifying the correlation in primary energy consumptions among the neighboring countries. Considering the diversity of primary energy composition, this study selects 6 components of primary energy, including oil, coal, natural gas, nuclear energy, hydropower and renewable energy as characteristic variables. A collaborative prediction model based on SVR for primary energy consumption prediction is proposed to explore the correlation of primary energy consumption among three countries in China, Japan and South Korea. The results show that there is a strong correlation between primary energy consumption when multiple countries make collaborative prediction, among which the primary energy consumption of South Korea has the largest impact on the primary energy consumption of China and Japan. In the primary energy cooperation of China-Japan-South Korea, a primary energy cooperation system with the South Korea as the link should be established through regional coordination to alleviate the shortage of traditional fossil energy.
ARTICLE | doi:10.20944/preprints201911.0231.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: solar uv radiation; Italy; Europe; ozone; aerosols; clouds; omi; dwd uv forecast
Online: 19 November 2019 (10:45:25 CET)
Review of the existing bibliography shows that the direction and magnitude of the long-term trends of UV irradiance, and their main drivers, vary significantly throughout Europe. Analysis of total ozone and spectral UV data recorded at four European stations during 1996 – 2017 reveals that long-term changes in UV are mainly driven by changes in aerosols, cloudiness, and surface albedo, while changes in total ozone play a less significant role. The variability of UV irradiance is large throughout Italy due to the complex topography and large latitudinal extension of the country. Analysis of the spectral UV records of the urban site of Rome, and the alpine site of Aosta reveals that differences between the two sites follow the annual cycle of the differences in cloudiness and surface albedo. Comparisons between the noon UV index measured at the ground at the same stations and the corresponding estimates from the DWD forecast model and OMI/Aura observations reveal differences of up to 6 units between individual measurements, which are likely due to the different spatial resolution of the different datasets, and average differences of 0.5 – 1 unit, possibly related to the use of climatological surface albedo and aerosol optical properties in the retrieval algorithms.
ARTICLE | doi:10.20944/preprints202308.1298.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: land atmosphere coupling metrics; soil ensemble forecast; soil moisture; planetary boundary layer; rainstorm
Online: 18 August 2023 (08:07:49 CEST)
This study has simulated the typical rainstorm on 20 July 2021 over central east China by using the first-generation Chinese Reanalysis datasets and Global Land Data Assimilation System datasets, and the Noah land surface model coupled with the advanced weather research and forecasting model. Based on this, the gridded planetary boundary layer (PBL) profiles and ensemble states within soil perturbations are collected to investigate the main land-atmosphere coupling characteristics during this modeled rainstorm by using various local coupling metrics and the introduced ensemble statistical metrics. Results have shown that (1) except for the stratospheric thermodynamics and surface thermal over mountain areas, the main characteristics of mid-low layers and surface have been well documented in this modeled rainstorm; (2) the typical coupling intensity is characterized by the dominant morning moistening, early noon weak PBL warming around 2, noontime buoyant mixing temperature deficit around 274 K, daytime PBL and surface latent flux contribution around 100 and 280 W/m2 respectively, and significant afternoon soil-surface latent flux coupling; (3) moist static energy is more significant than PBL height during the relation chains, which is consistent with the significance of surface moistening indicated by local coupling metrics. In general, wet soil contributes greatly to daytime moisture evaporation, which then increases the early noon PBL warming and enhances the noontime buoyant mixing within weak flux contribution. However, this has been suppressed by large-scale forcing such as the upper southwestern inflows of rainstorms, which has further significantly shaped the spatial distribution of statistical metrics in contrast. These quantitatively described local couplings have highlighted both the convection potential diagnoses usage for the local weather application and more applicable coupling threshold diagnoses within the finer spatial investigation.
ARTICLE | doi:10.20944/preprints202004.0175.v1
Subject: Medicine And Pharmacology, Pulmonary And Respiratory Medicine Keywords: coronavirus; statistical analysis; extrapolation; parameter estimation; pandemic spreading; virus; forecast; time evolution; dynamics
Online: 11 April 2020 (01:25:26 CEST)
We propose a Gauss model (GM), a map from time to the bell-shaped Gauss function to model the deaths per day and country, as a quick and simple model to make predictions on the coronavirus epidemic. Justified by the sigmoidal nature of a pandemic, i.e. initial exponential spread to eventual saturation, we apply the GM to existing data, as of April 2, 2020, from 25 countries during first corona pandemic wave and study the model's predictions. We find that logarithmic daily fatalities caused by Covid-19 are well described by a quadratic function in time. By fitting the data to second order polynomials from a statistical chi2-fit with 95\% confidence, we are able to obtain the characteristic parameters of the GM, i.e. a width, peak height and time of peak, for each country separately, with which we extrapolate to future times to make predictions. We provide evidence that this supposedly oversimplifying model might still have predictive power and use it to forecast the further course of the fatalities caused by Covid-19 per country, including peak number of deaths per day, date of peak, and duration within most deaths occur. While our main goal is to present the general idea of the simple modeling process using GMs, we also describe possible estimates for the number of required respiratory machines and the duration left until the number of infected will be significantly reduced.
ARTICLE | doi:10.20944/preprints201804.0162.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: operational forecast sytem; fire modeling; numerical weather prediction; high spatial reoslution; WRF-Fire
Online: 12 April 2018 (08:03:28 CEST)
Wildland fires are responsible for large socio-economic impacts. Fires affect the environment, damage structures, threaten lives, cause health issues, and involve large suppression costs. These impacts can be mitigated via accurate fire spread forecast to inform the incident management team. We show that a fire forecast system based on a numerical weather prediction (NWP) model coupled with a wildland fire behavior model can provide this forecast. This is illustrated with the Chimney Tops II wildland fire responsible for large socio-economic impacts. The system is run at high horizontal resolution (111 m) over the region affected by the fire to provide a fine representation of the terrain and fuel heterogeneities and explicitly resolve atmospheric turbulence. Our findings suggest that one can use the high spatial resolution winds, fire spread and smoke forecast to minimize the adverse impacts of wildland fires.
ARTICLE | doi:10.20944/preprints202309.1076.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: deep learning; Ensemble Forecast; GEFSv12; extended range time scale; Hybrid Postprocessing; maximum temperature; Taiwan
Online: 18 September 2023 (05:48:44 CEST)
Taiwan is highly susceptible to global warming, experiencing a 1.4°C increase in air temperatures from 1911-2005, which is twice the average for the Northern Hemisphere. This has led to higher rates of respiratory and cardiovascular mortality. Accurately predicting maximum temperatures during the summer season is crucial, but numerical weather models become less accurate and more uncertain beyond five days. To improve forecast reliability, statistical post-processing is needed to address systematic errors. In September 2020, NOAA NCEP implemented the Global Ensemble Forecast System version 12 (GEFSv12) to help manage climate risks. This study developed a Hybrid statistical post-processing method that combines Artificial Neural Networks (ANN) and Quantile mapping (QQ) approaches to predict daily maximum temperatures and extremes in Taiwan during the summer season. The Hybrid technique, utilizing deep learning techniques, was applied to the GEFSv12 reforecast data and evaluated against ERA5 reanalysis. The Hybrid technique was the most effective among the three techniques tested. It had the lowest bias, RMSE, and highest correlation coefficient. It successfully reduced the warm bias and overestimation of Tmax extreme days. This led to improved prediction skills for all forecast lead times. Compared to ANN and QQ, the Hybrid method was more effective in predicting summer daily Tmax and its extremes on an extended-range time scale deterministic and ensemble probabilistic forecasts over Taiwan.
ARTICLE | doi:10.20944/preprints202212.0292.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: statistical methods; flexible treatments; the middle-lower reaches of the Yangtze River; precipitation forecast
Online: 16 December 2022 (03:05:45 CET)
The multiple regression method is still an important tool for establishing precipitation forecast models with a lead time of one season. This study developed a flexible statistical forecast model for July precipitation over the middle-lower reaches of the Yangtze River (MLYR) based on the prophase winter sea surface temperature (SST). According to the characteristics of observed samples and related theoretical knowledge, some special treatments (i.e., more flexible and better–targeted methods) were introduced in the forecast model. These special treatments include a flexible MLYR domain definition, the extraction of indicative signals from the SST field, artificial samples, and the amplification of abnormal precipitation. Rolling forecast experiments show that the linear correlation between prediction and observation is around 0.5, more than half of the abnormal precipitation years can be successfully predicted, and there is no contradictory prediction of the abnormal years. These results indicate that the flexible statistical forecast model is valuable in real-life applications. Furthermore, sensitivity experiments show that forecast skills without these special treatments are obviously decreased. This suggests that forecast models can benefit from using statistical methods in a more flexible and better-targeted way.
ARTICLE | doi:10.20944/preprints202208.0389.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: Numerical weather prediction; Time integration; Filtering; Laplace transform; semi-implicit; semi-Lagrangian; Forecast accuracy
Online: 23 August 2022 (03:13:59 CEST)
A time integration scheme based on semi-Lagrangian advection and Laplace transform adjustment has been implemented in a baroclinic primitive equation model. The semi-Lagrangian scheme makes it possible to use large time steps. However, errors arising from the semi-implicit scheme increase with the time step size. In contrast, the errors using the Laplace transform adjustment remain relatively small for typical time steps used with semi-Lagrangian advection. Numerical experiments confirm the superior performance of the Laplace transform scheme relative to the semi-implicit reference model. The algorithmic complexity of the scheme is comparable to the reference model, making it computationally competitive, and indicating its potential for integrating weather and climate prediction models.
ARTICLE | doi:10.20944/preprints202207.0290.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: forecast; Earth Observation; time series; Snow Line Elevation; Alps; mountains; environmental modeling; machine learning
Online: 19 July 2022 (14:20:12 CEST)
Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we therefore explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985-2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5-8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash-Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolut Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a Combined forecast based on the best performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022-2029. In the majority of the catchments the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.
ARTICLE | doi:10.20944/preprints202008.0295.v1
Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: cut-off lows; circulation patterns; heavy precipitation; floods; forecast skill; unified model; GPM precipitation
Online: 13 August 2020 (08:10:27 CEST)
Mid-tropospheric cut-off low (COL) pressure systems are linked to severe weather, heavy rainfall and extreme cold conditions over South Africa. They often result in floods and snowfalls in winter disrupting economic activities. This paper examines the evolution and circulation patterns associated with severe COLs over South Africa. We evaluate the performance of the 4.4 km Unified Model (UM) which is currently used operationally by the South African Weather Service to simulate daily rainfall. Circulation variables and precipitation simulated by the UM were compared against ECMWF’s ERA Interim reanalyses and GPM precipitation at 24-hour timesteps. We present five recent (2016-2019) severe COLs that had high impact and found higher model skill when simulating heavy precipitation during the initial stages than the dissipating stages of the systems. A key finding was that the UM underestimated precipitation mainly due to inaccurate placing of COL centers and areas of heavy rainfall by up to 5° of latitude away from the actual location, due to the poor formulating of cumulus and microphysics schemes in the model. Understanding the performance and limitations of the UM model in simulating COL characteristics can benefit severe weather forecasting and contribute to disaster risk reduction in South Africa.
ARTICLE | doi:10.20944/preprints202307.1567.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: Energy consumption prediction; Energy management; Time series forecasting; Building energy consumption forecast; Covid-19 pandemic
Online: 24 July 2023 (08:47:09 CEST)
The Covid-19 pandemic and the subsequent implementation of lockdown measures have significantly impacted global electricity consumption, necessitating accurate energy consumption forecasts for optimal energy generation and distribution during a pandemic. In this study, we propose a new forecasting model called the Multivariate Multilayered LSTM with Covid-19 case injection ($\proposedModel$) for improved energy forecast during the next occurrence of a similar pandemic. We utilize data from commercial buildings in Melbourne, Australia during the Covid-19 pandemic to predict energy consumption and evaluate the model's performance against commonly used methods such as LSTM, Bi-LSTM, Linear Regression, Support Vector Machine and the previously published work of Multilayered LSTM (M-LSTM). The proposed forecasting model was analyzed using the following metrics of mean percent absolute error (MPAE), normalized root mean square error (NRMSE), and $R^2$ score values. The model $\proposedModel$ demonstrates superior performance, achieving the lowest MPAE values of 0.061, 0.093, and 0.158 for data sets from 3 different buildings, respectively. Our results highlight the improved precision and accuracy of the model, providing valuable information for energy management and decision-making during the challenges posed by the occurrence of a pandemic like Covid-19 in the future.
ARTICLE | doi:10.20944/preprints201702.0080.v1
Subject: Environmental And Earth Sciences, Environmental Science Keywords: ROS; snow; rain; flood; WRF; numerical weather forecast; energy balance; discharge estimation; early alert system
Online: 22 February 2017 (04:26:49 CET)
From June 18 to 19, 2013, the Ésera river in the Pyrenees, Northern Spain, caused widespread damage due to flooding as a result of torrential rains and sustained snowmelt. We estimate the contribution of snow melt to total discharge applying a snow energy balance to the catchment. Precipitation is derived from sparse local measurements and the WRF-ARW model over three nested domains, down to a grid cell size of 2 km. Temperature profiles, precipitation and precipitation gradient are well simulated, although with a possible displacement regarding the observations. Snowpack melting was correctly reproduced and verified in three instrumented sites, and according to satellite images. We found that the hydrological simulations agree well with measured discharge. Snowmelt represented 33% of total runoff during the main flood event and 23% at peak flow. The snow energy balance model indicates that most of the energy for snow melt during the day of maximum precipitation came from turbulent fluxes. This approach forecast correctly peak flow and discharge during normal conditions at least 24h in advance and could give an early warning of the extreme event 2.5 days before.
ARTICLE | doi:10.20944/preprints202101.0375.v1
Subject: Business, Economics And Management, Business And Management Keywords: cold chain logistics of agricultural products; demand forecast; principal component analysis, multiple linear regression, neural network.
Online: 19 January 2021 (11:50:09 CET)
Cold chain logistics of Agricultural Products demand forecasting can provide the scientific basis for the country to formulate logistics strategy, which further promotes the development of social economy and the improvement of living standards in China. In this paper, a new mathematical combined model is proposed to Agricultural Products Demand. Shandong, one of a China’s province, serves as the main producer and distributor of agricultural products. Based on the index system created from multiple related factors influencing cold chain logistics demand of agricultural products in Shandong, this paper employs principal component analysis to reduce the dimension of various indexes and predicts principal components with time series. Thereafter, multiple linear regression model and neural network model were constructed to forecast the cold chain logistics demand of agricultural products in Shandong, and their combined forecast models were compared. What's more, the paper provides insight for reference and decision-making concerning the development of cold chain logistics industry of agricultural products in Shandong province.
ARTICLE | doi:10.20944/preprints202301.0533.v1
Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: reservoir computing; deep echo state network; neuronal similarity-based iterative pruning merging algorithm; chaotic time series forecast
Online: 30 January 2023 (02:34:13 CET)
Recently, a layer-stacked ESN model named deep echo state Network (DeepESN) has been established. As an interactional model of recurrent neural network and deep neural network, investigations of DeepESN are of significant importance in both areas. Optimizing the structure of neural networks remains a common task in artificial neural networks, and the question of how many neurons should be used in each layer of DeepESN must be stressed. In this paper, our aim is to solve the problem of choosing the optimized size of DeepESN. Inspired by the sensitive iterative pruning algorithm, a neuronal similarity-based iterative pruning merging algorithm (NS-IPMA) is proposed to iteratively prune or merge the most similar neurons in DeepESN. Two chaotic time series prediction tasks are applied to demonstrate the effectiveness of NS-IPMA. The results show that the DeepESN pruned by NS-IPMA outperforms unpruned DeepESN with the same network size, and NS-IPMA is a feasible and superior approach to improving the generalization performance of DeepESN.
ARTICLE | doi:10.20944/preprints202202.0143.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: photovoltaic (PV) power forecast; multiple PV forecasting; short-term PV forecasting; motion estimation; optical flow; smart grid
Online: 10 February 2022 (02:22:32 CET)
The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method has been previously proposed by our laboratory for geographically distributed PV systems using motion estimation. This study focuses on an important parameter for estimating the proposed motion and optimizing the parameter. This parameter is important because it is associated with the smoothness of the vector field, which is the result of motion estimation and influences the forecasting accuracy. In the periods with drastic power output changes, the evaluation was conducted on 101 PV systems located within a circle of 15-km radius in the Kanto region of Japan. The results indicate that the absolute mean error of the proposed method with the optimized parameter is 10.3%, whereas that of the persistent prediction method is 23.7%. Therefore, the proposed method is effective in forecasting for periods when PV output changes drastically in a short time.
ARTICLE | doi:10.20944/preprints202006.0353.v1
Subject: Computer Science And Mathematics, Analysis Keywords: COVID-19; Epidemiology; COVID-19 Analysis and Forecast in Pakistan; Forecasting; Estimation; ARIMA; Prophet; SIRD; Diffusion; Analysis
Online: 29 June 2020 (10:50:47 CEST)
The COVID-19 infections in Pakistan are spreading at an exponential rate and a point may soon be reached where rigorous prevention measures would need to be adopted. Mathematical models can help define the scale of an epidemic and the rate at which an infection can spread in a community. I used ARIMA Model, Diffusion Model, SIRD Model and Prophet Model to forecast the magnitude of the COVID-19 pandemic in Pakistan and compared the numbers with the reported cases on the national database. Results depicts that Pakistan could hit peak number of infectious cases between June 2020 and July, 2020.
ARTICLE | doi:10.20944/preprints202202.0101.v2
Subject: Engineering, Civil Engineering Keywords: artificial intelligence; climate forecast; deep learning; ensemble model; multi-layer perceptron; neural network; regression; soil temperature; stacking method
Online: 17 February 2022 (09:56:27 CET)
Soil temperature is a fundamental parameter in water resources and engineering. A cost-effective model which can forecast soil temperature accurately is extensively needed. Recently, many studies have applied artificial intelligence (AI) at both surface and underground levels for soil temperature prediction. However, there is no comprehensive and detailed assessment of the performance of different AI approaches in soil temperature estimation, and primarily limited atmospheric variables are used as input data for AI models. In the present study, great varieties of various land and atmospheric variables are applied to evaluate the performance of a wide range of AI methods on soil temperature prediction. Herein, thirteen approaches, from classic regressions to well-established methods of random forest and gradient boosting to advanced AI techniques like multi-layer perceptron and deep learning are taken into account. The results show that AI is a promising approach in climate parameter forecast and deep learning demonstrates the best performance among other models. It has the highest R-squared ranging from 0.957 to 0.980, the lowest NRMSE ranging from 2.237% to 3.287% and the lowest MAE, ranging from 0.510 to 0.743 in predicting soil temperature. The prediction is repeated for different sizes of data, and prediction outcomes confirm the conclusion mentioned above.
ARTICLE | doi:10.20944/preprints202006.0063.v1
Subject: Computer Science And Mathematics, Probability And Statistics Keywords: COVID-19; Real-Time Tracker; Common Symptoms; Data Visualization; Hypothesis Testing; ARIMA Time-Series Forecast; Penalized Logistic Regression
Online: 7 June 2020 (07:44:48 CEST)
While the COVID-19 outbreak was reported to first originate from Wuhan, China, it has been declared as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 by WHO, and it has spread to over 180 countries by the time of this paper was being composed. As the disease spreads around the globe, it has evolved into a worldwide pandemic, endangering the state of global public health and becoming a serious threat to the global community. To combat and prevent the spread of the disease, all individuals should be well-informed of the rapidly changing state of COVID-19. In the endeavor of accomplishing this objective, a COVID-19 real-time analytical tracker has been built to provide the latest status of the disease and relevant analytical insights. The real-time tracker is designed to cater to the general audience without advanced statistical aptitude. It aims to communicate insights through various straightforward and concise data visualizations that are supported by sound statistical foundations and reliable data sources. This paper aims to discuss the major methodologies which are utilized to generate the insights displayed on the real-time tracker, which include real-time data retrieval, normalization techniques, ARIMA time-series forecasting, and logistic regression models. In addition to introducing the details and motivations of the utilized methodologies, the paper additionally features some key discoveries that have been derived in regard to COVID-19 using the methodologies.
ARTICLE | doi:10.20944/preprints201804.0056.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: demand-side management; peak demand control; dynamic-interval density forecast; stochastic optimization; dimension reduction; battery energy-storage system (BESS)
Online: 4 April 2018 (08:37:59 CEST)
A Demand-side management technique are deployed along with battery energy-storage systems (BESSs) to lower the electricity cost by mitigating the peak load of a building. Most of the existing methods rely on manual operation of the BESS, or even an elaborate building energy-management system resorting to a deterministic method that is susceptible to unforeseen growth in demand. In this study we propose a real-time optimal operating strategy for BESS based on density demand forecast and stochastic optimization. This method takes into consideration uncertainties in demand when accounting for an optimal BESS schedule, making it robust compared to the deterministic case. The proposed method is verified and tested against existing algorithms. Data obtained from a real site in South Korea is used for verification and testing. The results show that the proposed method is effective, even for the cases where the forecasted demand deviates from the observed demand
ARTICLE | doi:10.20944/preprints201711.0098.v1
Subject: Business, Economics And Management, Econometrics And Statistics Keywords: Paris 2015 Agreement; CO2 emissions; VAR models; Granger causality; impulse response functions; forecast error variance decomposition; software: R; MTS; RATS
Online: 15 November 2017 (18:34:09 CET)
In this paper a dynamic relationship between the CO2 emissions in Finland, Norway and Sweden is presented. With the help of a VAR(2) model, and using the Granger terminology, it is shown that the emissions in Finland are affecting those in Norway and Sweden. Other aspects of this dynamic relationship are presented as well.
Subject: Business, Economics And Management, Econometrics And Statistics Keywords: Efficiency of mutual funds, a deep crisis in the economy, efficiency forecast, model formation, financial investments, estimated income, mutual funds, CAPM, neural networks
Online: 31 March 2021 (22:04:28 CEST)
. In this article, a search for a calculation method and an analysis of performance indicators of mutual investment funds is carried out. Many factors can influence the return on investments in portfolio investments, which makes the choice of the fund incredibly difficult. However, in addition to the fact that it is difficult to determine which indicators should be given more attention and which should be omitted, it is not so easy to get these data. Some of them are publicly available on the Internet, while others can only be found in trading systems that are not accessible to people outside of this area. The article proves that a well-trained neural network can easily find existing patterns between risk and expected return on investment. It is a well-trained neural network that provides the ability to use the "what-if" function to justify your choice on real factors, as well as the ability to download available data and calculate the estimated income and its changes. This makes it much easier to choose a Fund, especially for inexperienced investors. The article also presents the results of a study of the dependence of estimated income on correlation, standard deviation, and volatility using a trained neural network. According to the theory, higher values of these three factors correspond to a higher amount of income. The obtained graphs of the calculated income dependence on correlation, standard deviation, and volatility confirmed the correctness of the neural network training and compliance with the relations described in the theory. The paper presents graphs of the dependence of the estimated income on the beta and alpha coefficients. The higher the beta and alpha indicators, the higher the expected return on investment. This corresponds to the dependency accepted in the model. When the values of the beta and alpha coefficients increase, the income also increases, which is completely consistent with the theory.
ARTICLE | doi:10.20944/preprints202103.0774.v1
Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Efficiency of mutual funds; a deep crisis in the economy; efficiency forecast; model formation; financial investments; estimated income; mutual funds; CAPM; neural networks
Online: 31 March 2021 (14:54:57 CEST)
In this article, a search for a calculation method and an analysis of performance indicators of mutual investment funds is carried out. Many factors can influence the return on investments in portfolio investments, which makes the choice of the fund incredibly difficult. However, in addition to the fact that it is difficult to determine which indicators should be given more attention and which should be omitted, it is not so easy to get these data. Some of them are publicly available on the Internet, while others can only be found in trading systems that are not accessible to people outside of this area. The article proves that a well-trained neural network can easily find existing patterns between risk and expected return on investment. It is a well-trained neural network that provides the ability to use the "what-if" function to justify your choice on real factors, as well as the ability to download available data and calculate the estimated income and its changes. This makes it much easier to choose a Fund, especially for inexperienced investors. The article also presents the results of a study of the dependence of estimated income on correlation, standard deviation, and volatility using a trained neural network. According to the theory, higher values of these three factors correspond to a higher amount of income. The obtained graphs of the calculated income dependence on correlation, standard deviation, and volatility confirmed the correctness of the neural network training and compliance with the relations described in the theory. The paper presents graphs of the dependence of the estimated income on the beta and alpha coefficients. The higher the beta and alpha indicators, the higher the expected return on investment. This corresponds to the dependency accepted in the model. When the values of the beta and alpha coefficients increase, the income also increases, which is completely consistent with the theory.
ARTICLE | doi:10.20944/preprints201806.0164.v1
Subject: Engineering, Civil Engineering Keywords: Integrated water resources management; support to decision-making process, streamflow forecast; simple and low-cost forecasting model; Guadalquivir River Basin; Genil River; Canales reservoir; Quéntar reservoir
Online: 11 June 2018 (16:40:22 CEST)
Forecasting streamflow accurately is essential to achieve an efficient integrated water resources management strategy and provide consistent support to water decision-makers. We present a simple, low-cost and robust approach for forecasting monthly and yearly streamflow during the hydrological year in course, applicable to headwater catchments. It combines the use of regression analysis techniques, the two-parameter Gamma continuous cumulative probability distribution function and the Monte Carlo method. It is based on a probabilistic comparison of the progression of the current hydrological year with the historic observed series. The methodology has been successfully applied to two headwater reservoirs within the Guadalquivir River Basin in southern Spain. The root-mean-square error and correlation coefficient were used to measure the accuracy of the model and the results showed good levels of reliability. The outputs are the probabilistic monthly and yearly streamflow and 80% confidence interval. Further reductions in prediction errors may be achieved from increasing the number of observed years. These risk-based predictions are of great value, especially, before the intensive irrigation campaign starts (usually in April), when Water Authorities are to take responsible management decisions about the best allocation of the available water volume between the different water users and environmental needs.
ARTICLE | doi:10.20944/preprints201806.0254.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: demand-side management; peak demand control; dynamic-interval density forecast; stochastic optimization; dimension reduction; battery energy-storage system (BESS), plugged-in electric vehicles (PEV); vehicle-to-grid (V2G); building energy-management systems (BEMS)
Online: 15 June 2018 (13:01:42 CEST)
This study purposes the use of plug-in electric vehicles for demand side management (DSM) considering uncertainties in demand as well as uncertainties due to mobility of PEV to mitigate peak demand. The solution also seeks to reduce electric cost in addition to reducing the effects of greenhouse gases. In recent years DSM using distributed storage system such as battery energy management system (BESS) and plugged-in electric vehicles (PEV) have become very prevalent with most implementations resorting to deterministic load forecast. These methods do not consider the potential growth in demand making their solutions less robust. In this study we propose a real-time density demand forecast and stochastic optimization for robust operation of PEV for a building. This method accounts for demand uncertainties in addition to uncertainties in mobile energy storage as found in PEV, making the resulting solution robust as compared to the deterministic case. A case study on a real site in South Korea is used for verification and testing. The proposed study is verified and tested against existing algorithms. The result verifies the effectiveness of the proposed approach