Forecasting the Electricity Capacity and Electricity Generation Values of Wind &Solar Energy with Artificial Neural Networks Approach: The Case of Germany


APPLIED ARTIFICIAL INTELLIGENCE, vol.36, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 36
  • Publication Date: 2022
  • Doi Number: 10.1080/08839514.2022.2033911
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Psycinfo, Civil Engineering Abstracts
  • Gazi University Affiliated: Yes


Recently, studies on energy estimation have been developing rapidly to increase the efficiency of Wind & Solar energy production-consumption. Artificial Neural Networks, an algorithm based on the human brain and its nervous system inspired by the data transfer and storage mechanism, can work very well as a prediction model. In this study, total Wind & Solar Electricity Capacity (WSEC) and total Wind & Solar Electricity Generation (WSEG) values of Germany, a G8 member and a European country, have been estimated by using Artificial Neural Networks (ANN) method. Population, unemployment, GDP growth and total renewable energy capacity (excluding wind and solar energy total) parameters have been used as input variables in ANN calculations. The use of geographic, socio-economic and technological parameters has strengthened the estimation model. WSEC training and test regressions calculated by ANN have been 1 and 0.99988, respectively. WSEC Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) parameters have been calculated as 94.783, 62496.807, 249.994 and 0.364, respectively. WSEG training and test regressions values have been 1 and 0.99983, respectively. The WSEG MAD, MSE, RMSE and MAPE parameters have been calculated as 114.406, 59252.128, 243.418 and 0.526, respectively.