Forecasting Wind and Solar Energy Production in the Greek Power System using ANN Models

Abstract

Renewable energy sources (RES) like solar and wind are quite uncertain because of the unpredictable nature of wind and sunlight. As a result, there are at present several issues with system security and the transformed structure of the energy market due to the increasing utilization of renewable energy sources (wind and solar). Accurate forecasting of renewable energy production is extremely important to ensure that the produced energy is equal to the consumed energy. Any deviations have an impact on the system's stability and could potentially cause a blackout in some situations. The issue of the high penetration of RES is discussed in this study along with a novel method of predicting them using artificial neural networks (ANN). The SARIMA prediction model is contrasted with the ANN approach. The suggested ANN for wind power plants has a mean average prediction error (MAPE) of 3%–4.3%, whereas the SARIMA model has a MAPE of 5%–6.5%. In comparison, the present prediction approaches typically have a MAPE of 5%–10%. When the MAPE of solar power plants was calculated, it was also discovered that the SARIMA model had a MAPE of 2.3%–4% and the suggested ANN had a MAPE of 1.4%–2.3%, whereas the MAPE of the present prediction methods was often about 9%.