Convolutional and LSTM Neural Networks for Solar Power Forecasting

Solar energy is one of the most promising renewable energy sources. However, it is highly variable, which motivates the development of accurate methods for forecasting the generated solar power, to facilitate its integration into the power grid. In this paper, we consider the task of predicting the half-hourly PV solar power for the next day, from three data sources: previous solar power, previous weather data and weather forecast for future days. We investigate the potential of LSTM and CNN neural networks, and also propose the new method LSTM-Conv, which leverages the strengths of LSTM to learn temporal dependencies and of CNN to learn useful features. The evaluation is conducted on two solar power datasets, for two years. The results showed that LSTM-Conv was the most accurate method, outperforming LSTM, CNN, MLP, RNN and a persistence baseline. The best result on both datasets was achieved by LSTM-Conv with weather data. The use of weather data considerably improved the results, highlighting its importance for day-ahead solar power forecasting.