Novel machine learning methods for short-term solar PV forecasting

Abstract

With the growing global drive to act up on climate change, the adoption of renewable energy sources such as solar photovoltaic (PV) is continuously increasing. This crucial shift poses many economic and environmental benefits, however the variability in solar PV generation may also threaten the stability of our power grid and energy supply. The reliable prediction of this fluctuating power resource on various time scales has been identified as a crucial technology for the continuous massive adoption of solar PV. This study concentrates on the application of convolutional neural networks (CNN) and Long Short Term Memory (LSTM) to process real-time data sources in spatially aggregated solar PV power forecast for Germany, with specifically a forecast horizon of 3 hours and 15-minute interval. Two models are designed to be applicable in a real-time operational setting with a short forecast lag: (1) A LSTM network that leverages on the latest solar PV generation data and a NWP based day-ahead power forecast, and (2) a CNN-LSTM network designed to utilize the latest satellite images and a NWP based day-ahead power forecast. The accuracy of the forecast models are evaluated using one year of solar PV power generation data in Germany (January 2020 through December 2020), and are compared to a persistence model and a NWP based day-ahead and intra-day power forecast provided by the German transmission system operators. The empirical results show that the two proposed models perform equal or better than the benchmark models. An implication for power trading practices is that deep learning models, such as LSTM and CNN-LSTM, shows to be a promising forecasting technique which deserves a place in a comprehensive solar PV power forecasting toolbox.