Forecasting Turkish electricity consumption: A critical analysis of single and hybrid models

Forecasting of electricity consumption is a critical issue, due to its importance in the planning of the energy trading countries. Several new techniques such as hybrid models are used as well as classical single models to estimate electricity consumption. This study aims to get the best electricity consumption model of Türkiye. For this, the forecasting performances of single and hybrid electricity consumption models, SARIMA is the time series model, ANNs and MLPs are machine learning single models and SARIMA-ANNs and SARIMA-MLPs are hybrid models of machine learning, are compared. This study employs new hybrid models and examines whether the multiplicative model of Wang et al. or the combined model of Khashei and Bijari is superior to than Zhang's hybrid model commonly used as the ARIMA-hybrid model with well known flaws. The results show that hybrid models are more accurate than single time series/machine learning models when forecasting Turkish electricity consumption. Moreover, The Khashei and Bijari hybrid model outperformed the other models and it was determined as the best model for forecasting Türkiye's electricity consumption. •In addition to single time series and machine learning models, three types of hybrid models were examined: Zhang, Wang et al. and Khashei and Bijari.•RMSE, MAE, R-squared criteria and two types of Diebold-Mariano test are used for model comparison.•The hybrid models are more accurate than single time series/machine learning models.•ANN GRprop algorithm is a more efficient algorithm than the back-propagation.•The Khashei and Bijari is the most successful hybrid model for forecasting the Türkiye's electricity consumption.