Day-ahead solar photovoltaic energy forecasting based on weather data using LSTM networks: a comparative study for photovoltaic (PV) panels in Turkey

Abstract

Photovoltaic (PV) panels are used to generate electricity by using solar energy from the sun. Although the technical features of the PV panel affect energy production, the weather plays the leading influential role. In this study, taking into account the power of the PV panels, the solar energy value it produces and the weather-related features, day-ahead solar photovoltaic energy forecasting is carried out over three different long short-term memory (LSTM) networks: LSTM, bidirectional long short-term memory (BiLSTM) and stacked LSTM. Finally, a comparative study with LSTM, BiLSTM and stacked LSTM models is constructed by using an actual dataset obtained with a 1741-day-long for 26 different panels on an inverter in İstanbul, Turkey. Stacked LSTM model for PV power forecasting predictions show that the average performance metrics root mean squared error of the 26 datasets reach to 19.17 kWe and 18.92 kWe for 50 and 100 epochs, respectively. The findings suggest that the proposed model is a reliable technique for solar photovoltaic energy prediction. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.