深層学習を用いたパネル上の積雪率予測による太陽光発電電力量予測の高精度化

抄録

In this research, we propose a novel method to accurately predict snow cover rate on photovoltaic panels in snowy areas for precise photovoltaic power generation predicting. Our proposed method utilizes weather data and past snow cover rates as input into a deep learning model composed of an autoencoder for compressing weather data dimensions and a bidirectional LSTM network for time series modeling. The deep learning model can predict hourly snow cover rates during daylight hours for the target prediction period. In experiments, we compare the prediction accuracy against using various data patterns and various deep learning models. As a result of the evaluation, the proposed method achieved the highest accuracy. Moreover, applying the predicted snow cover rates to photovoltaic power predicting substantively improves accuracy compared to using only weather data. These results demonstrate the effectiveness of the proposed method.