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Electricity Load Forecasting via ANN Approach in Turkish Electricity Markets
(Ankara Üniversitesi, 2020-12-31) Gözksöz, Fazıl; Filiz, Fahrettin; İşletme; Siyasal Bilgiler Fakültesi
Forecasting electricity load has become the essential task for electric utilities, power plants and regulators. It is essential that electricity load forecasts, which are a vital necessity of energy policies, produce healthy and reliable results. Artificial neural networks (ANN) can learn complex and nonlinear relationships. This article introduces 400 different ANN models for electricity load forecasting. Model performances have compared with Mean Absolute Percentage Error (MAPE) and Diebold-Mariano (DM) test. The electricity load data used for this study range from 2014 to 2016. The variation in forecasting ability of ANN for different models has also discussed. Levenberg-Marquardt (LM) with log-sigmoid transfer function trains the best performance ANN model.