Version 1
: Received: 8 June 2023 / Approved: 9 June 2023 / Online: 9 June 2023 (07:20:58 CEST)
How to cite:
Kim, Y.; Kim, N.; Park, S.; Kim, J.; Kim, C.; Oh, M.; Kim, H.; Lee, Y. Developing Prediction Models for Solar Photovoltaic Energy Generation Using GBM. Preprints2023, 2023060679. https://doi.org/10.20944/preprints202306.0679.v1
Kim, Y.; Kim, N.; Park, S.; Kim, J.; Kim, C.; Oh, M.; Kim, H.; Lee, Y. Developing Prediction Models for Solar Photovoltaic Energy Generation Using GBM. Preprints 2023, 2023060679. https://doi.org/10.20944/preprints202306.0679.v1
Kim, Y.; Kim, N.; Park, S.; Kim, J.; Kim, C.; Oh, M.; Kim, H.; Lee, Y. Developing Prediction Models for Solar Photovoltaic Energy Generation Using GBM. Preprints2023, 2023060679. https://doi.org/10.20944/preprints202306.0679.v1
APA Style
Kim, Y., Kim, N., Park, S., Kim, J., Kim, C., Oh, M., Kim, H., & Lee, Y. (2023). Developing Prediction Models for Solar Photovoltaic Energy Generation Using GBM. Preprints. https://doi.org/10.20944/preprints202306.0679.v1
Chicago/Turabian Style
Kim, Y., Hyun-Goo Kim and Yung-Seop Lee. 2023 "Developing Prediction Models for Solar Photovoltaic Energy Generation Using GBM" Preprints. https://doi.org/10.20944/preprints202306.0679.v1
Abstract
As renewable energy generation prediction system has been introduced into the energy trading market, making a model to accurately predict the quantity of solar photovoltaic (PV) energy generation has become a significant problem. Moreover, to encourage an accurate prediction of the quantity of energy generation, an incentive system has been implemented for those who predict the quantity of solar PV energy under the error rate of 8%. Therefore, it has become more important to investigate and analyze current prediction technology numerically and develop more advanced prediction system. In this study, we tried to develop a better model to improve the accuracy of solar PV energy generation quantity by comparing three models made with gradient boosting machine (GBM), Model 1, Model 2, Model 3 respectively. Model 1 was built with the whole training data set without any additional preprocessing. After conducting some additional preprocessing procedure to predict solar energy generation more accurately, we made Model 2 with the whole training data set and Model 3 with only upper 10% of energy generation capacity. To compare the accuracy of three models, normalized mean absolute error (nMAE) was used as an evaluation index. The nMAE of Model 1 was 9.64% while the Model 2 showed 8.41%. Also, Model 3, which was constructed with the training set of upper 10% energy generation capacity, outperformed with the nMAE of 8.08%. For further study, to check the effectiveness of models constructed with GBM, a time series model, autoregressive integrated moving average (ARIMA), was also built and the nMAE was compared.
Keywords
renewable energy; solar photovoltaic energy generation; prediction; gradient boosting machine (GBM); gradient boosting regressor (GBR), time series analysis; autoregressive integrated moving average (ARIMA); normalized mean absolute error (nMAE)
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.
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