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Preprint ARTICLE | doi:10.20944/preprints201903.0131.v1

Energy Consumption Prediction Using Machine Learning; A Review

Amir Mosavi, Abdullah Bahmani

Subject: Engineering, Energy And Fuel Technology Keywords: energy consumption; prediction; machine learning models; deep learning models; 21 artificial intelligence (AI); computational intelligence (CI); forecasting; soft computing (SC)

Online: 11 March 2019 (10:09:33 CET)

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Preprint ARTICLE | doi:10.20944/preprints201811.0096.v1

Machine Learning Models for Sales Time Series Forecasting

Bohdan M. Pavlyshenko

Subject: Computer Science And Mathematics, Information Systems Keywords: machine learning; stacking; forecasting; regression; sales; time series

Online: 5 November 2018 (09:54:54 CET)

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Preprint ARTICLE | doi:10.20944/preprints201609.0053.v1

Applying Multi-Output Random Forest Models to Electricity Price Forecast

Camino González, José M. Mira, José A. Ojeda

Subject: Engineering, Control And Systems Engineering Keywords: electricity markets; price forecasting; multi-output models; random forests; conditional inference trees

Online: 18 September 2016 (06:16:19 CEST)

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Preprint REVIEW | doi:10.20944/preprints201810.0098.v2

Flood Prediction Using Machine Learning, Literature Review

Amir Mosavi, Pinar Ozturk, Kwok-wing Chau

Subject: Environmental And Earth Sciences, Environmental Science Keywords: flood prediction; machine learning; forecasting

Online: 26 October 2018 (11:56:27 CEST)

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Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

Preprint ARTICLE | doi:10.20944/preprints201607.0001.v1

Forecasting Wholesale Electricity Prices With Artificial Intelligence Models: The Italian Case

Murad Harasheh

Subject: Business, Economics And Management, Finance Keywords: PUN, artificial intelligence models, regression tree, bootstrap aggregation, forecasting error

Online: 2 July 2016 (03:48:36 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201804.0286.v1

Electricity Price Forecasting Using Recurrent Neural Networks

Umut Ugurlu, Ilkay Oksuz, Oktay Tas

Subject: Business, Economics And Management, Finance Keywords: electricity price forecasting; deep learning; gated recurrent units; long short term memory; artificial intelligence, turkish day-ahead market

Online: 23 April 2018 (11:38:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201609.0103.v1

Predicting the Outcome of NBA Playoffs Based on Maximum Entropy Principle

Ge Cheng, Zhenyu Zhang, Moses Ntanda Kyebambe, Nasser Kimbugwe

Subject: Computer Science And Mathematics, Computational Mathematics Keywords: Maximum entropy model; K-means clustering; accuracy; classification; sports forecasting

Online: 27 September 2016 (11:10:50 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202004.0257.v1

Predictive Analytics of COVID-19 Using Information, Communication and Technologies

Parikshit N. Mahalle, Nilesh P. Sable, Namita P. Mahalle, Gitanjali R. Shinde

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: COVID-19; predictive analytics; forecasting models; Machine Learning Method; prediction; epidemic; pandemic

Online: 16 April 2020 (05:54:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201703.0231.v1

China's Copper Demand Forecasting Based on System Dynamics Model

Jianbo Yang, Xin Li, Qunyi Liu

Subject: Chemistry And Materials Science, Materials Science And Technology Keywords: copper resources; demand forecasting; system dynamics model; sustainability development

Online: 31 March 2017 (10:50:56 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201710.0133.v3

Comparison of Stochastic and Machine Learning Methods for Multi-Step Ahead Forecasting of Hydrological Processes

Georgia Papacharalampous, Hristos Tyralis, Demetris Koutsoyiannis

Subject: Engineering, Civil Engineering Keywords: multi-step ahead forecasting; neural networks; random forests; stochastic vs machine learning models; support vector machines; time series

Online: 17 January 2019 (15:51:22 CET)

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Preprint ARTICLE | doi:10.20944/preprints201609.0104.v1

Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

Tiannan Ma, Dongxiao Niu

Subject: Computer Science And Mathematics, Computer Science Keywords: icing forecasting; fireworks algorithm; least square support vector machine; feature selection

Online: 27 September 2016 (11:16:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201609.0031.v1

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

Bijay Neupane, Wei Lee Woon, Zeyar Aung

Subject: Computer Science And Mathematics, Computer Science Keywords: electricity price forecasting; ensemble model; expert selection

Online: 8 September 2016 (11:52:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202003.0096.v1

Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting

Pedro Lara-Benítez, Manuel Carranza-García, José M. Luna-Romera, José C. Riquelme

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deep learning; Energy demand; Temporal convolutional network; Time series forecasting

Online: 5 March 2020 (15:02:37 CET)

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Preprint ARTICLE | doi:10.20944/preprints201711.0190.v2

Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

Atul Anand, L Suganthi

Subject: Engineering, Energy And Fuel Technology Keywords: electricity demand; ANN; PSO; GA; hybrid optimization; forecasting

Online: 16 January 2018 (07:44:04 CET)

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Preprint ARTICLE | doi:10.20944/preprints201609.0119.v1

Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network

Cheng-Ming Lee, Chia-Nan Ko

Subject: Engineering, Electrical And Electronic Engineering Keywords: short-term load forecasting; radial basis function neural network; support vector regression; particle swarm optimization; adaptive annealing learning algorithm

Online: 29 September 2016 (12:22:20 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201608.0204.v1

A Grey Forecasting Approach for the Sustainability Performance of Logistics Companies

Min-Chun Yu, Chia-Nan Wang, Nguyen-Nhu-Y Ho

Subject: Business, Economics And Management, Economics Keywords: logistics industry; sustainability; data envelopment analysis (DEA); grey forecasting

Online: 25 August 2016 (10:12:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201707.0013.v1

Interannual Variability and Seasonal Predictability of Wind and Solar Resources

Nir Y. Krakauer, Daniel S. Cohan

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: renewable energy; solar; wind; interannual variability; seasonal forecasting; teleconnections

Online: 7 July 2017 (03:55:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201710.0051.v1

Resource Demand Forecasting Model Based on Dynamic Cloud Workload

Chunyan An, Jiantao Zhou

Subject: Computer Science And Mathematics, Computational Mathematics Keywords: cloud computing; workload model; workload-aware resource forecasting model

Online: 9 October 2017 (12:40:34 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0934.v1

Algorithm Optimizer in GA-LSTM for Stock Price Forecasting

Yohanes Leonardus Sukestiyarno, Dian Tri Wiyanti, Lathifatul Azizah

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Time Series; Forecasting, Deep Learning; Genetic Algorithm; Long Short-Term Memory

Online: 12 May 2023 (11:07:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202004.0063.v1

A Proposal for Isotherm World Maps to Forecast the Seasonal Evolution of the SARS-CoV-2 Pandemic

Nicola Scafetta

Subject: Biology And Life Sciences, Virology Keywords: SARS-CoV-2; COVID-19; Pandemic geographical distribution; Epidemic forecasting; Weather conditions; Climatic zones.

Online: 6 April 2020 (14:11:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201707.0006.v1

Forecasting Based on High-Order Fuzzy-Fluctuation Trends and Particle Swarm Optimization Machine Learning

Jingyuan Jia, Aiwu Zhao, Shuang Guan

Subject: Computer Science And Mathematics, Mathematics Keywords: Fuzzy forecasting, fuzzy-fluctuation trend, particle swarm optimization, fuzzy time series, fuzzy logical relationship

Online: 4 July 2017 (16:35:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201810.0494.v1

Unsupervised Feature Learning in Time Series Prediction Using Continuous Deep Belief Network

Qili Chen, Guangyuan Pan, Ming Yu, Jiuhe Wang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: unsupervised training; features learning; deep learning; time series forecasting

Online: 22 October 2018 (12:24:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201801.0051.v1

Smart Building: Use of the ANN Approach for Indoor Temperature Forecasting

Nivine Attoue, Isam Shahrour, Rafic Younes

Subject: Engineering, Control And Systems Engineering Keywords: smart building; artificial neural network (ANN); indoor; temperature; façade; outdoor; forecasting; relevance; sensors; recorded data

Online: 8 January 2018 (08:58:09 CET)

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Preprint ARTICLE | doi:10.20944/preprints201710.0053.v1

Time Series Seasonal Analysis Based on Fuzzy Transforms

Ferdinando Di Martino, Salvatore Sessa

Subject: Computer Science And Mathematics, Computer Science Keywords: ARIMA; forecasting; fuzzy partition; fuzzy transform; time series

Online: 9 October 2017 (16:58:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201801.0216.v1

Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

Atul Anand, L Suganthi

Subject: Engineering, Energy And Fuel Technology Keywords: Electricity Demand; ANN; PSO; GA; Hybrid Optimization; Forecasting

Online: 23 January 2018 (15:30:10 CET)

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Preprint ARTICLE | doi:10.20944/preprints202108.0246.v1

Explaining Bad Forecasts in Global Time Series Models

Jože Rožanec, Elena Trajkova, Klemen Kenda, Blaž Fortuna, Dunja Mladenić

Subject: Engineering, Electrical And Electronic Engineering Keywords: Explainable Artificial Intelligence; XAI; Time Series Forecasting; Global Time Series Models; Machine Learning; Artificial Intelligence

Online: 11 August 2021 (10:32:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201710.0129.v1

Error Evolution in Multi-Step Ahead Streamflow Forecasting for the Operation of Hydropower Reservoirs

Georgia Papacharalampous, Hristos Tyralis, Demetris Koutsoyiannis

Subject: Engineering, Civil Engineering Keywords: hydropower; errors; multi-step ahead forecasting; recursive method; simulations

Online: 19 October 2017 (02:34:27 CEST)

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Working Paper ARTICLE

Very Short-term Forecasting of Wind Power Generation using Hybrid Deep Learning Model

Md. Alamgir Hossain, Ripon K. Chakrabortty, Sondoss Elsawah, Michael J. Ryan

Subject: Engineering, Electrical And Electronic Engineering Keywords: wind power forecasting; short-term prediction; hybrid deep learning; wind farm; long short term memory; gated recurrent network and convolutional layers

Online: 22 September 2020 (03:45:59 CEST)

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Preprint TECHNICAL NOTE | doi:10.20944/preprints202209.0404.v1

Forecasting Energy Consumption Time Series Using Recurrent Neural Network in Tensorflow

Munshi Md Shafwat Yazdan, Marzieh Khosravia, Shah Saki, Md Abdullah Al Mehedi

Subject: Engineering, Energy And Fuel Technology Keywords: Recurrent Neural Network; Renewable Energy; Power consumption; Open Power System Data; Multivariate Exploratory; Time series forecasting

Online: 27 September 2022 (02:44:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201806.0365.v1

Time Series Forecasting Using a Two-level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans

Yamur K. Al-Douri, Hussan Hamodi, Jan Lundberg

Subject: Engineering, Control And Systems Engineering Keywords: ARIMA model; data forecasting; multi-objective genetic algorithm; regression model

Online: 24 June 2018 (07:48:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202006.0353.v1

Analysis and Forecast of COVID-19 Pandemic in Pakistan

Abdul Bari Malik

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)

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Preprint ARTICLE | doi:10.20944/preprints202011.0205.v1

Implementation of Water Demand Forecasting Model to Aid Sustainable Water Supply Chain Management in UAE

Vian Ahmed, Ahmad Saad, Hasan Saleh, Sara Saboor, Nikita Kasianov, Tahani Alnaqbi

Subject: Engineering, Control And Systems Engineering Keywords: Neural Networks; Long-Short Term Model; Water demand; Forecasting; Sustainable development goals; Water Goal.

Online: 5 November 2020 (10:17:30 CET)

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Preprint ARTICLE | doi:10.20944/preprints201708.0086.v1

Forecasting Algorithms for Recurrent Patterns in Consumer Demand

Tetiana Boiko, Oleg Karpenkov, Bulat Rakhimberdiev

Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: seasonality; forecasting; pull and push models; denoising

Online: 25 August 2017 (08:21:40 CEST)

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Preprint REVIEW | doi:10.20944/preprints201812.0217.v1

Computational Intelligence Load Forecasting: A Methodological Overview

Seyedeh Narjes Fallah, Mehdi Ganjkhani, Shahab Shamshirband, Kwok-wing Chau

Subject: Engineering, Electrical And Electronic Engineering Keywords: Intelligent Load Forecasting; Demand-Side Management; Pattern Similarity; Hierarchical Forecasting; Feature Selection; Weather Station Selection

Online: 18 December 2018 (10:38:10 CET)

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Preprint ARTICLE | doi:10.20944/preprints202103.0310.v1

Unfolding Jellyfish Bloom Dynamics Along the Mediterranean Basin by Transnational Citizen Science Initiatives

Macarena Marambio, Antonio Canepa, Laura Lopez, Adam Gauci, Sonia Gueroun, Serena Zampardi, Ferdinando Boero, Ons Kefi-Daly Yahia, Mohamed Nejib Daly Yahia, Veronica Fuentes, Stefano Piraino, Alan Deidun

Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Gelatinous zooplankton; Scyphozoa; Pelagia noctiluca; Rhizostoma pulmo; Forecasting system; Mitigation tool; Coastal zone management

Online: 11 March 2021 (10:57:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints201812.0201.v1

Bayesian VAR Modeling and Forecasting of the Dynamic Interrelationship between Economic Growth and Revenue from Oil and Non-Oil Sectors in Nigeria

Monday Osagie Adenomon

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: Bayesian Vector Autoregression (BVAR), Modeling, Forecasting, Gross Domestic Product (GDP), Economic Growth, Revenue, Oil sector, Non-oil Sector

Online: 17 December 2018 (15:56:09 CET)

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Preprint ARTICLE | doi:10.20944/preprints201807.0440.v2

Ownership Cost Comparison of Battery Electric and Non-Plugin Hybrid Vehicles: A Consumer Perspective

Lawrence Fulton

Subject: Engineering, Automotive Engineering Keywords: BEV; ownership cost analysis; design of experiments; forecasting; Monte Carlo simulation

Online: 17 August 2018 (12:59:31 CEST)

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Preprint REVIEW | doi:10.20944/preprints201905.0175.v1

Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances

Amir Mosavi, Sina Faizollahzadeh Ardabili, Shahabodin Shamshirband

Subject: Engineering, Electrical And Electronic Engineering Keywords: demand prediction, energy systems; machine learning; artificial neural network (ANN); support vector machines (SVM); neuro-fuzzy; ANFIS; wavelet neural network (WNN); big data; decision tree (DT); ensemble learning; hybrid models; data science; deep learning; renewable energies; energy informatics; prediction; forecasting; energy demand

Online: 14 May 2019 (14:00:40 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201811.0490.v1

Forecasting Performances of the Reduced Form VAR and Sims-Zha Bayesian VAR Models when the Multiple Time Series are Jointly Influenced by Collinearity and Autocorrelated Error

Monday Osagie Adenomon, Benjamin Agboola Oyejola

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: forecasting; time series; vector autoregression (VAR), bayesian VAR; collinearity and autocorrelation

Online: 20 November 2018 (09:03:49 CET)

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Preprint ARTICLE | doi:10.20944/preprints202004.0397.v1

Forecasting COVID-19-Associated Hospitalizations under Different Levels of Social Distancing in Lombardy and Emilia-Romagna, Northern Italy: Results from an Extended SEIR Compartmental Model

Chiara Reno, Jacopo Lenzi, Antonio Navarra, Eleonora Barelli, Davide Gori, Alessandro Lanza, Riccardo Valentini, Biao Tang, Maria Pia Fantini

Subject: Medicine And Pharmacology, Epidemiology And Infectious Diseases Keywords: coronavirus; COVID-19; SARS-CoV-2; pandemic; public health intervention; lockdown; resurgence; forecasting; mathematical modelling; SEIR model

Online: 22 April 2020 (08:48:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201807.0019.v1

Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-up Forecasting

Benjamin Auder, Jairo Cugliari, Yannig Goude, Jean-Michel Poggi

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Clustering; Forecasting; Hierarchical Time-Series; Individual Electrical Consumers; Scalable; Short Term; Smart Meters; Wavelets

Online: 2 July 2018 (17:43:29 CEST)

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Working Paper ARTICLE

Design Of a Dynamic and Self-Adapting System, Supported With Artificial Intelligence, Machine Learning and Real-Time Intelligence For Predictive Cyber Risk Analytics in Extreme Environments- Cyber Risk in the Colonisation of Mars

Petar Radanliev

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Artificial intelligence; machine learning; real-time probabilistic data; for cyber risk; super forecasting; red teaming;

Online: 12 April 2021 (12:18:14 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202009.0228.v1

Forecasting COVID 19 Confirmed Cases Using Machine Learning: the Case of America

Mario Jojoa, Begoña Garcia-Zapirain

Subject: Engineering, Control And Systems Engineering Keywords: multilayer perceptron; support vector machine; COVID19; SarsCov2; forecasting; machine learning; public health; pandemic

Online: 10 September 2020 (08:05:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202009.0491.v1

Short-Term Firm-Level Energy Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models

Andrea MariaN.C. Ribeiro, Pedro RafaelX.do Carmo, Iago Rodrigues, Djamel Sadok, Theo Lynn, Patricia Takako Endo

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Short term load forecasting; STLF; deep learning; RNN; LSTM; GRU; machine learning; SVR; random forest; KNN; energy consumption; energy-intensive manufacturing; time series prediction; industry

Online: 21 September 2020 (04:19:45 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201808.0507.v1

Hydrologic Forecasts Verification and Comparison of Forecasting Methods

Sergei Borsch, Andrei Khristoforov, Vladimir Krovotynzev, Ekaterina Leontieva, Yuri Simonov, Victoria Deriugina

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)

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Working Paper ARTICLE

Predicting Compression Index of Fine-Grained Soils Using Gene Expression Programming Model

Danial Mohammadzadeh, Seyed-Farzan Kazemi, Amir Mosavi, Ehsan Nasseralshariati

Subject: Engineering, Civil Engineering Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

Online: 25 March 2019 (10:25:18 CET)

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Preprint ARTICLE | doi:10.20944/preprints202211.0278.v1

LSTM Applied for Commodity Futures Price Forecast

Gabriel Schneider de Jesus, Aurélio Faustino Hoppe, Andreza Sartori, Valderi Reis Quietinho Leithardt

Subject: Computer Science And Mathematics, Computer Science Keywords: Long Short-Term Memory; time series forecasting; commodities; technical analysis

Online: 15 November 2022 (07:00:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints201808.0163.v1

A Long Short-term Traffic Flow Prediction Method Optimized by Cluster Computing

Boyi Liu, Jieren Cheng, Qiang Liu, Xiangyan Tang

Subject: Engineering, Electrical And Electronic Engineering Keywords: traffic flow forecasting; cluster computing; LSTM neural networks

Online: 8 August 2018 (08:50:09 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201905.0079.v1

Prediction of Multi-Inputs Bubble Column Reactor Using a Novel Hybrid Model of Computational Fluid Dynamics and Machine Learning

Amir Mosavi, Shahab Shamshirband, Ely Salwana, Kwok-wing Chau, Joseph H. M. Tah

Subject: Engineering, Civil Engineering Keywords: machine learning, computational fluid dynamics (CFD), hybrid model, adaptive neuro-fuzzy inference system (ANFIS), artificial intelligence, big data, prediction, forecasting, optimization, hydrodynamics, fluid dynamics, soft computing, computational intelligence, computational fluid mechanics

Online: 7 May 2019 (11:30:56 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202003.0158.v1

Energy Demand Forecasting Using Deep Learning: Application to the French Grid

Alejandro J. del Real, Fernando Dorado, Jaime Durán

Subject: Engineering, Energy And Fuel Technology Keywords: energy; demand; forecasting; deep; learning; machine; convolutional; artificial; neural; networks

Online: 10 March 2020 (03:40:31 CET)

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Preprint ARTICLE | doi:10.20944/preprints202010.0565.v1

Modeling and Forecasting of Lake Malombe Fish Biomass and Catch Per Unit Effort (CPUE)

Rodgers Makwinja, Seyoum Mengistou, Emmanuel Kaunda, Tena Alemiew, Titus Phiri, Ishmael Kosamu, Chikumbusko Kaonga

Subject: Biology And Life Sciences, Anatomy And Physiology Keywords: ARIMA, CPUE, Fish biomass landings, Forecasting, Lake Malombe, Time series approach

Online: 27 October 2020 (21:04:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202105.0116.v1

Forecasted Incidence, Intensive Care Unit Admissions and Projected Mortality Attributable to Covid-19 in Portugal, UK, Germany, Italy and France – 4 Weeks Ahead

Kathleen Carvalho, João Paulo Vicente, Mihajlo Jakovljevic, João Paulo Teixeira

Subject: Medicine And Pharmacology, Pulmonary And Respiratory Medicine Keywords: Time Series Prediction; ANN forecasting; New Coronavirus; COVID19 prediction cases; COVID19 prediction deaths; COVID19 prediction ICU, COVID19 Vaccination; COVID19 in Europe; COVID19 in Israel; COVID19 use of face mask.

Online: 6 May 2021 (16:58:01 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202308.0257.v1

LightGBM and SVM algorithms for Predicting Synthetic Load Profiles Using a Non-Intrusive Approach

Khumbulani Mpofu, Olukorede Tijani Adenuga, Olawale M Popoola, And Abel Mathebula

Subject: Engineering, Industrial And Manufacturing Engineering Keywords: Light Gradient Boosted; Machine learning; Non-Intrusive Approach; electric load forecasting; Support Vector Machine; Synthetic Load Profile.

Online: 3 August 2023 (10:37:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201806.0164.v1

Simple and Low-cost Model for Monthly and Yearly Streamflow Forecasts during the Current Hydrological Year

Fernando Delgado-Ramos, Carmen Hervás-Gámez

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)

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Preprint ARTICLE | doi:10.20944/preprints202201.0107.v1

Short and Very Short Term Firm-level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

Andrea Maria N. C. Ribeiro, Pedro Rafael X. do Carmo, Patricia Takako Endo, Pierangelo Rosati, Theo Lynn

Subject: Engineering, Energy And Fuel Technology Keywords: Very short term load forecasting; VSTLF; Short term load forecasting; STLF; deep learning; RNN; LSTM; GRU; machine learning; SVR; random forest; extreme gradient boosting, energy consumption; ARIMA; time series prediction.

Online: 10 January 2022 (12:17:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202001.0387.v1

Clustered Based Prediction for Batteries in the Data Centers

Syed Naeem Haider, Qianchuan Zhao, Xueliang Li

Subject: Engineering, Energy And Fuel Technology Keywords: forecasting; clustering; energy systems; classification

Online: 31 January 2020 (13:28:01 CET)

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Preprint REVIEW | doi:10.20944/preprints202303.0451.v1

Forecasting Renewable Energy Generation with Machine learning and Deep Learning: Current Advances and Future Prospects

Natei Ermias Benti, Mesfin Diro Chaka, Addisu Gezahegn Semie

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Accurate predictions; Deep Learning; Energy management; Machine Learning; Renewable En-ergy Forecasting

Online: 27 March 2023 (07:56:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201811.0010.v1

Forecasting Events in the Chaotic Dynamics of a Fiber Laser

Andrés Aragoneses, Yingqi Ding

Subject: Physical Sciences, Optics And Photonics Keywords: forecasting; complex dynamics; fiber laser; chaos; ordinal patterns

Online: 2 November 2018 (04:21:25 CET)

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Preprint ARTICLE | doi:10.20944/preprints202105.0261.v1

Assessing Point Forecast Bias Across Multiple Time Series: Measures and Visual Tools

Andrey Davydenko, Paul Goodwin

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)

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Preprint REVIEW | doi:10.20944/preprints201812.0235.v1

Computational Intelligence Load Forecasting: A Methodological Overview

Narjes Fallah, Mehdi Ganjkhani

Subject: Engineering, Electrical And Electronic Engineering Keywords: Intelligent Load Forecasting 1; Demand-Side Management 2; Pattern Similarity 3; Hierarchical Forecasting 4; Feature Selection 5; Weather Station Selection 6

Online: 19 December 2018 (12:19:14 CET)

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Preprint ARTICLE | doi:10.20944/preprints202204.0295.v1

Benchmark of Holt-Winters and SARIMA Methods in Predicting Jakarta Climate

Amartya Natayu, Quincy Julian H. Clarke, Muhamad Fikri

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: Forecasting; SARIMA; Holt-Winters; Climate; Big Data

Online: 29 April 2022 (08:44:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202009.0518.v1

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Elias Eze, Tahmina Ajmal

Subject: Computer Science And Mathematics, Information Systems Keywords: aquaculture water quality; dissolved oxygen (DO); forecasting; EEMD; LSTM

Online: 22 September 2020 (10:02:09 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201810.0625.v2

Introduction of an Experimental Terrestrial Forecasting/Monitoring System at Regional to Continental Scales Based on the Terrestrial System Modeling Platform (v1.1.0)

Stefan Kollet, Fabian Gasper, Slavko Brdar, Klaus Goergen, Harrie-Jan Hendricks-Franssen, Jessica Keune, Wolfgang Kurtz, Volker Küll, Florian Pappenberger, Stefan Poll, Silke Trömel, Prabhakar Shrestha, Clemens Simmer, Mauro Sulis

Subject: Environmental And Earth Sciences, Remote Sensing Keywords: terrestrial modeling; real-time forecasting/monitoring; workflows

Online: 16 November 2018 (08:06:18 CET)

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Preprint ARTICLE | doi:10.20944/preprints202104.0398.v1

A Two-Stage Polynomial and Nonlinear Growth Approach for Modeling the COVID-19 Outbreak in Mexico

Rafael Perez Abreu, Samantha Estrada, Héctor de-la-Torre-Gutiérrez

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: COVID-19; epidemic modeling; time series forecasting; nonlinear growth models; Prais-Winsten estimation

Online: 15 April 2021 (07:44:35 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201905.0320.v1

Support Vecztor Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

Saeed Samadianfard, Salar Jarhan, Ely Salwana, Amir Mosavi, Shahaboddin Shamshirband, Shatirah Akib

Subject: Engineering, Civil Engineering Keywords: river flow forecasting; optimization; M5 model tree; support vector regression; fruit fly optimization algorithm

Online: 27 May 2019 (13:01:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202012.0619.v1

An Adaptive and Resilient Islanding Approach for Power System Considering Secondary Collapses

Sanaullah Kaka, Mingchao Xia, Mazhar Hussain, Ali Raza Meer, Shubash Kumar

Subject: Engineering, Automotive Engineering Keywords: Adaptive Island; Minimum spanning tree; Load forecasting; Controllable switches; Distributed generations

Online: 24 December 2020 (12:35:42 CET)

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Preprint ARTICLE | doi:10.20944/preprints202103.0269.v1

Forecasting High-Frequency Financial Time Series: An Adaptive Learning Approach With the Order Book Data

Parley Ruogu Yang

Subject: Business, Economics And Management, Accounting And Taxation Keywords: forecasting methods; statistical learning; high-frequency order book

Online: 9 March 2021 (12:24:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints201907.0016.v1

Life Cycle Modeling of Structural Defects via Computational Geometry and Time Series Forecasting

Sara Mohamadi, David Lattanzi

Subject: Engineering, Civil Engineering Keywords: Remote sensing; Photogrammetry; Life cycle modeling; Time series forecasting; Structural damage; Stochastic modeling; Convex Hull; ARIMA; VAR; Fatigue crack prediction

Online: 1 July 2019 (12:23:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202110.0037.v1

Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations

Daisuke Kodaira, Kazuki Tsukazaki, Taiki Kure, Junji Kondoh

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)

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Preprint ARTICLE | doi:10.20944/preprints202204.0107.v1

On the Redox-Activity and Health-Effects of Atmospheric Primary and Secondary Aerosol: Phenomenology

Francesca Costabile, Stefano Decesari, Roberta Vecchi, Franco Lucarelli, Gabriele Curci, Dario Massabò, Matteo Rinaldi, Maurizio Gualtieri, Emanuela Corsini, Elena Menegola, Silvia Canepari, Lorenzo Massimi, Stefania Argentini, Maurizio Busetto, Gianluca Di Iulio, Luca Di Liberto, Marco Paglione, Igor Petenko, Mara Russo, Angela Marinoni, Gianpietro Casasanta, Sara Valentini, Vera Bernardoni, Federica Crova, Gianluigi Valli, Alice Corina Forello, Fabio Giardi, Silvia Nava, Giulia Pazzi, Paolo Prati, Virginia Vernocchi, Teresa La Torretta, Ettore Petralia, Milena Stracquadanio, Gabriele Zanini, Gloria Melzi, Emma Nozza, Martina Iulini, Donatella Caruso, Lucia Cioffi, Gabriele Imperato, Flavio Giavarini, Maria Battistoni, Francesca Di Renzo, Maria Agostina Frezzini, Cinzia Perrino, Maria Cristina Facchini

Subject: Environmental And Earth Sciences, Atmospheric Science And Meteorology Keywords: atmospheric aerosol; chemical composition; secondary aerosol; source apportionment; ultrafine particles; oxidative potential; exposure; toxicology; forecasting; micrometeorology

Online: 12 April 2022 (09:54:57 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202308.0292.v1

A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers

Sebastian C Ibañez, Christopher Pineda Monterola

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: crop production; agricultural production; time series forecasting; artificial intelligence; transformer; machine learning; deep learning

Online: 3 August 2023 (10:34:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201905.0361.v1

Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin

Saeed Samadianfard, Salar Jarhan, Ely Salwana, Amir Mosavi, Shahaboddin Shamshirband

Subject: Engineering, Civil Engineering Keywords: river flow forecasting; optimization; M5 model tree; support vector regression; fruit fly optimization algorithm

Online: 30 May 2019 (05:33:06 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201912.0102.v1

Predicting Environmental Contribution to Ship’s Average Fuel Consumption Based on Non-Uniform Time Set

Igor Vujović, Ivica Kuzmanić, Miro Petković, Joško Šoda

Subject: Engineering, Electrical And Electronic Engineering Keywords: forecasting of fuel demand; ship’s fuel consumption; data fitting; statistical quality measures; signal processing and analysis

Online: 8 December 2019 (17:05:03 CET)

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Preprint ARTICLE | doi:10.20944/preprints202304.0144.v1

Impact of Covid-19 Pandemic on Demand and Demand Forecasting in a Furniture Wholesale Company

Riadh Al-Haidari, Shrouq Al-Rawashdeh, Adam Zeidan, Joshua Omambala, Nagendra Nagarur

Subject: Engineering, Other Keywords: COVID-19; Machine learning; Demand Forecasting; Neural network; LSTM ;

Online: 10 April 2023 (04:27:16 CEST)

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Preprint REVIEW | doi:10.20944/preprints202307.1298.v1

Artificial Intelligence in Environmental Monitoring: Application of Artificial Neural Networks and Machine Learning for Pollution Prevention and Toxicity Measurements

Katarzyna Szramowiat-Sala

Subject: Environmental And Earth Sciences, Pollution Keywords: artiﬁcial neural networks; machine learning; environmental protection; air pollution; atmosphere; pollutants emission prediction; forecasting; data security

Online: 19 July 2023 (07:17:31 CEST)

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Environmental monitoring systems play a crucial role in assessing environmental quality, detecting limits exceedances, and predicting potential ecological episodes. These systems rely on the measurement of various variables at specific locations and time intervals over an extended period. The concept of environmental monitoring encompasses the assessment of health and safety issues for public and environmental health purposes. Pollution of the atmosphere and water, climate change, and natural disasters are among the consequences of continuous industrial and municipal development and human interference in natural ecosystems. To address these challenges and to protect human lives and the environment, with a special concern on mitigating the ecological effects of industrial development, advanced technical solutions, including the technologies associated with artificial intelligence (artificial neural networks ANNs, machine learning ML) have been developed. These technologies offer powerful tools for analysing the vast amount of data collected by monitoring systems and extracting valuable insights. By applying ANNs and machine learning algorithms, environmental monitoring systems can effectively process and interpret the measured variables to assess environmental quality. Despite challenges and limitations, such as data quality and interpretability of AI models, ongoing research and interdisciplinary collaboration are paving the way for the successful implementation of AI in environmental monitoring, ultimately supporting informed decision-making and sustainable resource management.While several review papers have explored the theory of artificial intelligence (AI), here I aim to review the application of ANNs and ML, in environmental aspects, specifically in automotive and industrial emissions toxicity measurements, as well as atmospheric pollution prevention. By examining the potential of AI in these domains, the paper contributes to understanding the role of advanced technologies in environmental monitoring and protection.

Preprint ARTICLE | doi:10.20944/preprints202103.0412.v2

Fighting Deepfakes Using Body Language Analysis

Robail Yasrab, Wanqi Jiang, Adnan Riaz

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Imaging; Machine learning; Deepfakes; Human pose estimation; Upper body languages; World leader; Deep learning; Computer vision; Recurrent Neural Networks (RNNs); Long Short-term Memory(LSTM); machine learning; Forecasting

Online: 28 April 2021 (12:02:00 CEST)

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Preprint REVIEW | doi:10.20944/preprints202305.1534.v1

A Review of State-of-the-art and Short-Term Forecasting Models for Solar PV Power Generation

Wen-Chang Tsai, Chia-Sheng Tu, Chih-Ming Hong, Whei-Min Lin

Subject: Engineering, Energy And Fuel Technology Keywords: Predictive models; Weather research and forecasting (WRF); Solar irradiance forecasting; Solar PV power forecasting; Renewable energy sources.

Online: 23 May 2023 (02:32:03 CEST)

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Working Paper ARTICLE

Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming

Danial Mohammadzadeh S., Seyed-Farzan Kazemi, Amir Mosavi, Ehsan Nasseralshariati

Subject: Engineering, Civil Engineering Keywords: Evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

Online: 25 March 2019 (10:21:45 CET)

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Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (C_c) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, C_c was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e₀). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate C_csuch as oedometer test. This study presents a novel prediction model for C_c of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate C_c based on LL, PL, and e₀. The performance of developed GEP-based model was evaluated through coefficient of determination (R²), root mean squared error (RMSE), and mean average error (MAE). High R² and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R², RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining C_c of fine-grained soils.Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

Preprint ARTICLE | doi:10.20944/preprints201810.0679.v1

The Relevance of Foreshocks in Earthquake Triggering: A Statistical Study

Eugenio Lippiello, Cataldo Godano, Lucilla De Arcangelis

Subject: Physical Sciences, Applied Physics Keywords: seismic forecasting; foreshocks; stochastic model

Online: 29 October 2018 (11:47:36 CET)

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Preprint ARTICLE | doi:10.20944/preprints201911.0149.v1

On Car Sharing Usage Prediction with Open Socio-Demographic Data

Michele Cocca, Douglas Teixeira, Luca Vassio, Marco Mellia, Jussara M. Almeida, Ana Paula Couto da Silva

Subject: Engineering, Control And Systems Engineering Keywords: car sharing; forecasting; machine learning; socio-demographic; weather

Online: 13 November 2019 (12:31:49 CET)

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Preprint ARTICLE | doi:10.20944/preprints202104.0332.v1

Performance Assessment for Short-Term Water Demand Forecasting Models at an End-Use Level in Korea

Kang Min Koo, Kuk Heon Han, Kyung Soo Jun, Gyumin Lee, Jung Sik Kim, Kyung Taek Yum

Subject: Engineering, Automotive Engineering Keywords: smart water grid; advanced metering infrastructure; short-term water demand forecasting; end-use level; on-site sodium hypochlorite generator

Online: 13 April 2021 (09:20:08 CEST)

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It is crucial to forecast the water demand accurately for supplying water efficiently and stably in a water supply system. In particular, accurately forecasting short-term water demand helps in saving energy and reducing operating costs. With the introduction of the Smart Water Grid (SWG) in a water supply system, the amount of water consumption is obtained in real time through an advanced metering infrastructure (AMI) sensor, which can be used for forecasting the short-term water demand. The models widely used for water demand forecasting include the autoregressive integrated moving average, radial basis function-artificial neural network, quantitative multi-model predictor plus, and long short-term memory. However, there is a lack of research on assessing the performance of models and forecasting the short-term water demand by applying the data on the amount of water consumption by purpose and the pipe diameter of an end-use level of the SWG demonstration plant in each demand forecasting model. Therefore, in this study, the short-term water demand was forecasted for each model using the data collected from the AMI, and the performance of each model was assessed. The Smart Water Grid Research Group installed ultrasonic-wave-type AMI sensors in the block 112 located in YeongJong Island, Incheon, and the actual data used for operating the SWG demonstration plant were adopted. The performance of the model was assessed by using the residual, root mean square error (RMSE), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (NSE), and Pearson correlation coefficient (PCC) as indices. The water demand forecast was slightly underestimated in models that employed the assessment results based on the RMSE and NRMSE. Furthermore, the forecasting accuracy was low for the NSE due to a large number of negative values; the correlation between the observed and forecasted values of the PCC was not high, and it was difficult to forecast the peak amount of water consumption. Therefore, as the short-term water demand forecasting models using only time and the amount of water consumption have limitations in reflecting the characteristics of consumers, a water supply system can be managed more precisely if other factors (weather, customer behavior, etc.) influencing the water demand are applied.

Preprint ARTICLE | doi:10.20944/preprints201909.0182.v1

Development of Korean Nomograms for Forecasting and Alerting Debris Flows Based on Critical Accumulated Rainfall

Dong-Ho Nam, Suk-Ho Lee, Byung-Sik Kim

Subject: Engineering, Civil Engineering Keywords: rainfall intensity; debris flow forecasting; rainfall triggering index (RTI); critical accumulated rainfall (Rc); nomogram

Online: 17 September 2019 (06:09:31 CEST)

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Preprint REVIEW | doi:10.20944/preprints202304.0917.v1

A Review of Modern Wind Power Generation Forecasting Technologies

Wen-Chang Tsai, Chih-Ming Hong, Whei-Min Lin, Chia-Sheng Tu, Chiung-Hsing Chen

Subject: Engineering, Energy And Fuel Technology Keywords: Predictive models; Weather research and forecasting (WRF); Uncertainty; Wind forecasting; Ultra short term and Short term; Wind power generation

Online: 25 April 2023 (10:10:36 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202306.1744.v1

Optimization of Resource Management Allocation for Enterprise Benefit Based on Macroeconomic Prediction Model

Qian Zhong

Subject: Business, Economics And Management, Business And Management Keywords: Macroeconomic Forecasting Model; Scheduling Algorithm; Enterprise Benefit Optimization; Resource Management

Online: 26 June 2023 (05:34:15 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202111.0510.v1

Flood Early Warning Systems using Machine Learning Techniques. Case the Tomebamba Catchment at the Southern Andes of Ecuador

Paul Muñoz, Johanna Orellana-Alvear, Jörg Bendix, Jan Feyen, Rolando Célleri

Subject: Environmental And Earth Sciences, Environmental Science Keywords: Flood Early Warning; forecasting; hydrological extremes; Machine Learning; Andes

Online: 26 November 2021 (13:30:09 CET)

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Preprint ARTICLE | doi:10.20944/preprints202104.0138.v1

Comparison of Forecasting Ability for Energy Consumption in BRICS: ARIMA (1,1,1) and FGM (1, 1) Models

Atif Maqbool Khan, Magdalena Osinska

Subject: Business, Economics And Management, Accounting And Taxation Keywords: Energy consumption; BRICS; GM (1, 1); Fractional-order; GREY; Forecasting accuracy

Online: 5 April 2021 (13:51:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202110.0090.v1

Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing

Patrik Zajec, Jože M. Rožanec, Elena Trajkova, Inna Novalija, Klemen Kenda, Blaž Fortuna, Dunja Mladenić

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: artificial intelligence; machine learning; active learning; knowledge acquisition; explainable artificial intelligence; manufacturing; demand forecasting; smart assistant

Online: 5 October 2021 (15:23:46 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202202.0192.v1

Inequalities in Temporal Effects on Cervical Cancer Mortality in States in Different Geographic Regions of Brazil: An Ecological Study

Karina Cardoso Meira, Carinne Magnago, Angelo Braga Mendonça, Stephane Fernanda Soares Duarte, Pedro Henrique Oliveira de Freitas, Juliano dos Santos, Dyego Leando Bezerra de Souza, Taynãna César Simões

Subject: Medicine And Pharmacology, Obstetrics And Gynaecology Keywords: uterine cervical neoplasms; mortality; age-period-cohort analysis; forecasting; Brazil

Online: 16 February 2022 (05:03:16 CET)

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Preprint REVIEW | doi:10.20944/preprints202304.1168.v1

Modes of Operation and Forcing in Oil Spill Modeling: State-of-Art, Deficiencies and Challenges

Panagiota Keramea, Nikolaos Kokkos, George Zodiatis, Georgios Sylaios

Subject: Environmental And Earth Sciences, Oceanography Keywords: oil spill modeling; meteorological and hydrodynamic forcing; wave models; met-ocean data; forecasting; biogeochemical models; oil biodegradation.

Online: 28 April 2023 (10:49:30 CEST)

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Preprint REVIEW | doi:10.20944/preprints202208.0031.v1

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

Nemesio Fava Sopelsa Neto, Stefano Frizzo Stefenon, Luiz Henrique Meyer, Raúl García Ovejero, Valderi Reis Quietinho Leithardt

Subject: Engineering, Electrical And Electronic Engineering Keywords: LSTM; GMDH; ANFIS; Ensemble Learning Models; Wavelet; Time Series Forecasting

Online: 2 August 2022 (03:50:14 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202007.0409.v1

Modeling and Detection of Future Cyber-Enabled DSM Data Attacks

Kostas Hatalis, Chengbo Zhao, Parv Venkitasubramaniam, Larry Snyder, Shalinee Kishore, Rick S. Blum

Subject: Engineering, Electrical And Electronic Engineering Keywords: Demand Side Management; Demand Response; Cyber-Physical Systems; Dynamic Pricing; Load Forecasting; Attack Detection

Online: 19 July 2020 (11:14:01 CEST)

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Demand-Side Management (DSM) is an essential tool to ensure power system reliability and stability. In future smart grids, certain portions of a customer’s load usage could be under the automatic control of a cyber-enabled DSM program, which selectively schedules loads as a function of electricity prices to improve power balance and grid stability. In this scenario, the security of DSM cyberinfrastructure will be critical as advanced metering infrastructure and communication systems are susceptible to cyber-attacks. Such attacks, in the form of false data injections, can manipulate customer load profiles and cause metering chaos and energy losses in the grid. The feedback mechanism between load management on the consumer side and dynamic price schemes employed by independent system operators can further exacerbate attacks. To study how this feedback mechanism may worsen attacks in future cyber-enabled DSM programs, we propose a novel mathematical framework for (i) modeling the nonlinear relationship between load management and real-time pricing, (ii) simulating residential load data and prices, (iii) creating cyber-attacks, and (iv) detecting said attacks. In this framework, we first develop time-series forecasts to model load demand and use them as inputs to an elasticity model for the price-demand relationship in the DSM loop. This work then investigates the behavior of such a feedback loop under intentional cyber-attacks. We simulate and examine load-price data under different DSM-participation levels with three types of random additive attacks: ramp, sudden, and point attacks. We conduct two investigations for the detection of DSM attacks. The first studies a supervised learning approach, with various classification models, and the second studies the performance of parametric and nonparametric change point detectors. Results conclude that higher amounts of DSM participation can exacerbate ramp and sudden attacks leading to better detection of such attacks, especially with supervised learning classifiers. We also find that nonparametric detection outperforms parametric for smaller user pools, and random point attacks are the hardest to detect with any method.

Preprint ARTICLE | doi:10.20944/preprints202301.0006.v1

Econometrics Analysis of Financial Risk and Earnings Management in Dual System Banking for Asean-5 Countries

Wita Ramadhanti, Sri Mangesti Rahayu, Safrina Muarrifah

Subject: Business, Economics And Management, Business And Management Keywords: Financial distress; Dual system banking; Loan Loss Provission; forecasting; econometrics

Online: 3 January 2023 (07:19:41 CET)

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Nowadays, many Muslim-majority countries have implemented a dual banking system, namely the sharia and conventional systems. The development of Islamic banks is to fulfill the Muslims' need for the existence of halal transactions in financial institutions. However, in some countries, it turns out that conventional banks still dominate the country's economy. Because of that, it is necessary to see whether there are differences in financial risk and Earnings management between Islamic and conventional banks. The samples are conventional and Islamic banks in Southeast Asia, analyzed by the purposive sampling method from 2010-2019. The analytical tool used is the statistical difference test and economometrics analysis using generalized least square (GLS) regression with panel data (time series and cross-sectional data). These models are intended to forecasting the macroeconomics effects in applying dual banking system in one country or region. The results using non parametrics means difference test showed that the first hypothesis is accepted It means that Earnings management in conventional banks is greater than in Islamic banks. The Random Model Effect (REM) for second and third hypotheses testing on Conventional banks shows the Bankruptcy Risk and NPL do not affect the dependent variable Earnings Management (LLP). While fixed effect model testing on Islamic banks, the second and third hypothesis testing is rejected. Therefor Islamic Banks the value of Bankruptcy Risk (z-score) and the value of Non-Performing Loans (NPL) do not affect Earnings management. It also means that hypothesis 2 and 3 are rejected both in conventional as well as Islamic Banking. Sensitivity analysis for conventional as well as Islamic banking altogether using fixed effect model shows that the second and third hypotheses show that the independent variables (Bankruptcy Risk and NPL) do not affect the dependent variable Earnings Management (LLP). These results can be concluded that Islamic bank are enganged in less earnings management. Therefor in the the long run there are still more research that should conduct in comparing dual banking system in one region.

Preprint ARTICLE | doi:10.20944/preprints202206.0086.v1

Leveraging Geographically Distributed Data for Influenza and SARS-CoV-2 Non-Parametric Forecasting

Pablo Boullosa, Adrián Garea, Iván Carlos Area-Carracedo, Juan Nieto, Jorge Mira

Subject: Computer Science And Mathematics, Computational Mathematics Keywords: non-parametric modeling; flu; influenza; COVID-19; SARS-CoV-2; Empirical Dynamic Modeling; forecasting

Online: 6 June 2022 (10:24:45 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202311.0794.v1

A Novel Deep-learning based Approach for Time Series Forecasting using SARIMA, Neural Prophet and Fb Prophet

Khulood Albeladi, Bassam Zafar, Ahmed Mueen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: time-series analysis; time-Series forecasting; artificial intelligence; SARIMA; neural prophet model; Fb prophet model; evaluation parameters

Online: 13 November 2023 (09:03:43 CET)

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Preprint ARTICLE | doi:10.20944/preprints202308.2119.v1

Renewable Energy Integration for Power Outage Mitigation: A Data-Driven Approach in Advancing Grid Resilience Strategies

Mahtab Murshed, Manohar Chamana, Konrad Schmitt, Suhas Pol, Olatunji Adeyanju, Stephen Bayne

Subject: Engineering, Electrical And Electronic Engineering Keywords: Grid resilience; Power outage prediction; Monte Carlo simulation; LSTM forecasting; Hybrid LSTM-PSO model; Battery State of Charge; Microgrid integration; Techno-economic analysis; Renewable energy; Energy independence

Online: 31 August 2023 (04:18:56 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202302.0315.v1

Characterising Hospital Admission Patterns and Length of Stay in the Emergency Department at Mater Dei Hospital Malta

Lalit Garg, Natasha Attard, Roberta J Caruana, Bhushan Dinkar Pawar, Sally I McClean, Sandra C Buttigieg, Neville Calleja

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Length of stay estimation; Admission rate characterization; Resource requirement forecasting; Patientcare modelling; Hospital capacity planning; Phase type survival trees; Machine Learning; Health ML Extended Health Intelligence

Online: 20 February 2023 (01:38:22 CET)

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Preprint ARTICLE | doi:10.20944/preprints202110.0302.v1

Application of ALO-ELM in Load Forecasting Based on Big Data

Ming He, Yi Li, Wan Zou, Xiangxi Duan

Subject: Engineering, Electrical And Electronic Engineering Keywords: load forecasting; extreme learning machine (ELM); ant lion optimization (ALO) ；parameter optimization; model.

Online: 21 October 2021 (09:34:56 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202208.0345.v1

SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting

Ghadah Alkhayat, Syed Hamid Hasan, Rashid Mehmood

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deep learning; solar radiation forecasting; model prediction; solar energy; multi climates data; generalizability; sustainability; Long Short-Term Memory (LSTM); Gated Recurrent Unit (GRU); Convolutional Neural Network (CNN); Hybrid CNN-Bidirectional LSTM; LSTM Autoencoder

Online: 18 August 2022 (10:59:18 CEST)

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The sustainability of the planet and its inhabitants is in dire danger and is among the highest priorities on global agendas such as the Sustainable Development Goals (SDGs) of the United Nations (UN). Solar energy -- among other clean, renewable, and sustainable energies -- is seen as essential for environmental, social, and economic sustainability. Predicting solar energy accurately is critical to increasing reliability and stability, and reducing the risks and costs of the energy systems and markets. Researchers have come a long way in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracies, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weathers due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for Sustainable Energy), a novel deep learning-based auto-selective approach and tool that, instead of generalising a specific model for all climates, predicts the best performing deep learning model for GHI forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyse the tool in great detail through a range of metrics and methods for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Forecast Skills (FS), Relative Forecasting Error, and the normalised versions of these metrics. The proposed auto-selective approach can be extended to other research problems such as wind energy forecasting and predict forecasting models based on different criteria (in addition to the minimum forecasting error used in this paper) such as the energy required or speed of model execution, different input features, different optimisations of the same models, or other user preferences.

Preprint REVIEW | doi:10.20944/preprints202309.1764.v2

Machine Learning Methods in Weather and Climate Applications: A Survey

Liuyi Chen, Bocheng Han, Xuesong Wang, Jiazhen Zhao, Wenke Yang, Zhengyi Yang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Machine-learning; Weather prediction; Climate prediction; Survey; Meteorological Forecasting

Online: 30 October 2023 (17:09:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints202210.0004.v1

Wavelet Long Short-Term Memory to Fault Forecasting in Electrical Power Grids

Nathielle Waldrigues Branco, Mariana Santos Matos Cavalca, Stefano Frizzo Stefenon, Valderi Reis Quietinho Leithardt

Subject: Engineering, Electrical And Electronic Engineering Keywords: Electrical Power Grids; Fault Forecasting; Long Short-Term Memory; Time Series Forecasting; Wavelet Transform

Online: 3 October 2022 (10:36:14 CEST)

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