Version 1
: Received: 30 October 2023 / Approved: 31 October 2023 / Online: 31 October 2023 (12:30:45 CET)
How to cite:
Hanif, M.F.; Naveed, S.; Si, J.; Liu, X.; Mi, J. Towards Optimal Solar Energy Integration: A Deep Dive into AI-Enhanced Solar Irradiance Forecasting Models. Preprints2023, 2023102071. https://doi.org/10.20944/preprints202310.2071.v1
Hanif, M.F.; Naveed, S.; Si, J.; Liu, X.; Mi, J. Towards Optimal Solar Energy Integration: A Deep Dive into AI-Enhanced Solar Irradiance Forecasting Models. Preprints 2023, 2023102071. https://doi.org/10.20944/preprints202310.2071.v1
Hanif, M.F.; Naveed, S.; Si, J.; Liu, X.; Mi, J. Towards Optimal Solar Energy Integration: A Deep Dive into AI-Enhanced Solar Irradiance Forecasting Models. Preprints2023, 2023102071. https://doi.org/10.20944/preprints202310.2071.v1
APA Style
Hanif, M.F., Naveed, S., Si, J., Liu, X., & Mi, J. (2023). Towards Optimal Solar Energy Integration: A Deep Dive into AI-Enhanced Solar Irradiance Forecasting Models. Preprints. https://doi.org/10.20944/preprints202310.2071.v1
Chicago/Turabian Style
Hanif, M.F., Xiangtao Liu and Jianchun Mi. 2023 "Towards Optimal Solar Energy Integration: A Deep Dive into AI-Enhanced Solar Irradiance Forecasting Models" Preprints. https://doi.org/10.20944/preprints202310.2071.v1
Abstract
Keywords: Artificial Neural Network (ANN), Support Vector Machine (SVM), Support Vector Regression (SVR), Lightweight Gradient Boosting Machines (Light GBM), Machine Learning, Solar Irradiance (SI), Solar forecasting
Keywords
artificial neural network (ANN); Support Vector Machine (SVM); Support Vector Regression (SVR); Lightweight Gradient Boosting Machines (Light GBM); Machine Learning; Solar Irradiance (SI); solar forecasting
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The commenter has declared there is no conflict of interests.
Comment:
This article represents a commendable effort in advancing the field of environmental modeling, particularly in the accurate prediction of Solar Irradiance (SI) using advanced machine learning techniques. The meticulous application of the RELAD-ANN and Light GBM models, along with the rigorous validation processes employed, showcases a high level of expertise and dedication to precision. but In your study, you employed the RELAD-ANN and Light GBM models for predicting Solar Irradiance (SI). However, there is no mention of considering atmospheric variables like cloud cover, precipitation, or aerosol levels, which can significantly impact SI. Could you clarify if these factors were integrated into your models, and if not, how do you account for their influence in your SI predictions?
Commenter's Conflict of Interests:
I am one of the author
Comment:
Your observation regarding the inclusion of atmospheric variables such as cloud cover, precipitation, and aerosol levels is indeed noteworthy, given their substantial impact on Solar Irradiance (SI). In the present study, as outlined in section 2.1, line 176, these factors were not directly integrated into our analysis using the RELAD-ANN and Light GBM models. Our analysis predominantly focused on quantifying the impact of more directly measurable parameters - namely, wind speed, specific humidity, and air temperature - on SI. Additionally, with reference to the local climate conditions in Quetta, characterized by an average humidity of 45% and wind speeds around 13 kph, the predominant sunny conditions imply minimal influence of cloud cover on SI, as corroborated by the "Anatomy of Zone Forecast. Furthermore, the region experiences relatively low precipitation levels.
Nonetheless, the importance of the aforementioned atmospheric variables in comprehensive SI modeling is recognized. Future extensions of this research endeavor will aim to incorporate these elements into our predictive frameworks, thereby augmenting both the precision and the scope of our models. While the current study's exclusion of these factors may pose certain constraints, the results obtained still provide meaningful insights into SI trends in relation to the environmental parameters considered. The prospective inclusion of a wider array of atmospheric variables promises to enrich our understanding of SI fluctuations and enhance the sophistication of our modeling approaches.
The commenter has declared there is no conflict of interests.
Comment:
Thank you for addressing the scope of atmospheric variables in your modeling of Solar Irradiance . The justification based on Quetta's climate for initially excluding variables like cloud cover is understood.
The commenter has declared there is no conflict of interests.
Comment:
I find the exploration of the relationship between Solar Irradiance (SI) and various environmental factors in this research to be relatively new. The study's focus on establishing correlations between SI and parameters like wind speed, specific humidity, and air temperature using the SVM and Light GBM models is a novel approach that enriches our understanding of these complex interactions.
Commenter:
The commenter has declared there is no conflict of interests.
Commenter:
Commenter's Conflict of Interests: I am one of the author
Nonetheless, the importance of the aforementioned atmospheric variables in comprehensive SI modeling is recognized. Future extensions of this research endeavor will aim to incorporate these elements into our predictive frameworks, thereby augmenting both the precision and the scope of our models. While the current study's exclusion of these factors may pose certain constraints, the results obtained still provide meaningful insights into SI trends in relation to the environmental parameters considered. The prospective inclusion of a wider array of atmospheric variables promises to enrich our understanding of SI fluctuations and enhance the sophistication of our modeling approaches.
Commenter:
The commenter has declared there is no conflict of interests.
Commenter:
The commenter has declared there is no conflict of interests.