Version 1
: Received: 2 November 2023 / Approved: 3 November 2023 / Online: 6 November 2023 (10:21:09 CET)
How to cite:
Kim, Y.; Kim, N.; Park, S.; Kim, C.K.; Oh, M.; Kim, H.; Kim, J.; Lee, Y. Developing Prediction Models for Solar Photovoltaic Energy Generation Using Statistical and Machine Learning Methods. Preprints2023, 2023110238. https://doi.org/10.20944/preprints202311.0238.v1
Kim, Y.; Kim, N.; Park, S.; Kim, C.K.; Oh, M.; Kim, H.; Kim, J.; Lee, Y. Developing Prediction Models for Solar Photovoltaic Energy Generation Using Statistical and Machine Learning Methods. Preprints 2023, 2023110238. https://doi.org/10.20944/preprints202311.0238.v1
Kim, Y.; Kim, N.; Park, S.; Kim, C.K.; Oh, M.; Kim, H.; Kim, J.; Lee, Y. Developing Prediction Models for Solar Photovoltaic Energy Generation Using Statistical and Machine Learning Methods. Preprints2023, 2023110238. https://doi.org/10.20944/preprints202311.0238.v1
APA Style
Kim, Y., Kim, N., Park, S., Kim, C.K., Oh, M., Kim, H., Kim, J., & Lee, Y. (2023). Developing Prediction Models for Solar Photovoltaic Energy Generation Using Statistical and Machine Learning Methods. Preprints. https://doi.org/10.20944/preprints202311.0238.v1
Chicago/Turabian Style
Kim, Y., Jin-Young Kim and Yung-Seop Lee. 2023 "Developing Prediction Models for Solar Photovoltaic Energy Generation Using Statistical and Machine Learning Methods" Preprints. https://doi.org/10.20944/preprints202311.0238.v1
Abstract
As renewable energy generation prediction systems have been introduced into the energy trading market, accurate prediction of solar photovoltaic (PV) energy generation has become a crucial challenge for ensuring stable trade of variable energy. Therefore, it is essential to quantitatively investigate and analyze current prediction technology and develop more advanced prediction systems. In this study, three models for predicting PV energy generation were investigated. Multiple regression, random forest, and gradient boosting machine (GBM) models were con-structed for 15 utility-scale power plants throughout South Korea. Model performance was evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE). The multiple regression-based model had an RMSE of 12.00% and MAE of 9.06%. The random forest model had an RMSE of 11.69% and MAE of 8.61%. The GBM model had an RMSE of 11.34% and MAE of 8.18%, indicating its superior accuracy among the three models in predicting PV energy generation. These findings provide valuable insights that may contribute to the development of advanced PV energy generation models and their application in grid operations.
Keywords
decision tree; gradient boosting machine (GBM); machine learning method; multiple regression analysis; photovoltaic plant; random forest; solar photovoltaic energy generation; statistical method
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
,
*
,
*
