Hybrid ensemble neural network approach for photovoltaic production forecast

With the main goal of saving the environment and reducing the amount of burnt fossil fuels, the penetration of renewable energy sources as a share of the electrical energy production is constantly increasing. However, this growth significantly jeopardizes electrical grid stability, since renewable sources highly depend on the meteorological conditions, which are stochastic by their nature. Therefore, careful planning of energy use is necessary, which is why a photovoltaic production forecaster model has been presented within this paper. The main focus was presenting a hybrid ensemble neural network approach which combines ensembling method with complex LSTM + CNN networks with the aim of improving forecasting performance. The approach has been tested using real-world year-long data from the town of Adeje in Tenerife and the results show an improvement in forecasting precision in comparison with the conventional ensemble model and with the hybrid approach on the test data, both state-of-the-art solutions.