@article{ART002869873},
author={Saem-Mi Lee and Kyu-Cheol Cho},
title={Prediction of Solar Photovoltaic Power Generation by Weather Using LSTM},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2022},
volume={27},
number={8},
pages={23-30},
doi={10.9708/jksci.2022.27.08.023}
TY - JOUR
AU - Saem-Mi Lee
AU - Kyu-Cheol Cho
TI - Prediction of Solar Photovoltaic Power Generation by Weather Using LSTM
JO - Journal of The Korea Society of Computer and Information
PY - 2022
VL - 27
IS - 8
PB - The Korean Society Of Computer And Information
SP - 23
EP - 30
SN - 1598-849X
AB - Deep learning analyzes data to discover a series of rules and anticipates the future, helping us in various ways in our lives. For example, prediction of stock prices and agricultural prices. In this research, the results of solar photovoltaic power generation accompanied by weather are analyzed through deep learning in situations where the importance of solar energy use increases, and the amount of power generation is predicted. In this research, we propose a model using LSTM(Long Short Term Memory network) that stand out in time series data prediction. And we compare LSTM’s performance with CNN(Convolutional Neural Network), which is used to analyze various dimensions of data, including images, and CNN-LSTM, which combines the two models. The performance of the three models was compared by calculating the MSE, RMSE, R-Squared with the actual value of the solar photovoltaic power generation performance and the predicted value. As a result, it was found that the performance of the LSTM model was the best. Therefor, this research proposes predicting solar photovoltaic power generation using LSTM.
KW - deep learning;prediction;weather;solar photovoltaic power generation;LSTM
DO - 10.9708/jksci.2022.27.08.023
ER -
Saem-Mi Lee and Kyu-Cheol Cho. (2022). Prediction of Solar Photovoltaic Power Generation by Weather Using LSTM. Journal of The Korea Society of Computer and Information, 27(8), 23-30.
Saem-Mi Lee and Kyu-Cheol Cho. 2022, "Prediction of Solar Photovoltaic Power Generation by Weather Using LSTM", Journal of The Korea Society of Computer and Information, vol.27, no.8 pp.23-30. Available from: doi:10.9708/jksci.2022.27.08.023
Saem-Mi Lee, Kyu-Cheol Cho "Prediction of Solar Photovoltaic Power Generation by Weather Using LSTM" Journal of The Korea Society of Computer and Information 27.8 pp.23-30 (2022) : 23.
Saem-Mi Lee, Kyu-Cheol Cho. Prediction of Solar Photovoltaic Power Generation by Weather Using LSTM. 2022; 27(8), 23-30. Available from: doi:10.9708/jksci.2022.27.08.023
Saem-Mi Lee and Kyu-Cheol Cho. "Prediction of Solar Photovoltaic Power Generation by Weather Using LSTM" Journal of The Korea Society of Computer and Information 27, no.8 (2022) : 23-30.doi: 10.9708/jksci.2022.27.08.023
Saem-Mi Lee; Kyu-Cheol Cho. Prediction of Solar Photovoltaic Power Generation by Weather Using LSTM. Journal of The Korea Society of Computer and Information, 27(8), 23-30. doi: 10.9708/jksci.2022.27.08.023
Saem-Mi Lee; Kyu-Cheol Cho. Prediction of Solar Photovoltaic Power Generation by Weather Using LSTM. Journal of The Korea Society of Computer and Information. 2022; 27(8) 23-30. doi: 10.9708/jksci.2022.27.08.023
Saem-Mi Lee, Kyu-Cheol Cho. Prediction of Solar Photovoltaic Power Generation by Weather Using LSTM. 2022; 27(8), 23-30. Available from: doi:10.9708/jksci.2022.27.08.023
Saem-Mi Lee and Kyu-Cheol Cho. "Prediction of Solar Photovoltaic Power Generation by Weather Using LSTM" Journal of The Korea Society of Computer and Information 27, no.8 (2022) : 23-30.doi: 10.9708/jksci.2022.27.08.023
