Comparing the Simple to Complex Automatic Methods with the Ensemble Approach in Forecasting Electrical Time Series Data

The importance of forecasting in the energy sector as part of electrical power equipment maintenance encourages researchers to obtain accurate electrical forecasting models. This study investigates simple to complex automatic methods and proposes two weighted ensemble approaches. The automated methods are the autoregressive integrated moving average; the exponential smoothing error–trend–seasonal method; the double seasonal Holt–Winter method; the trigonometric Box–Cox transformation, autoregressive, error, trend, and seasonal model; Prophet and neural networks. All accommodate trend and seasonal patterns commonly found in monthly, daily, hourly, or half-hourly electricity data. In comparison, the proposed ensemble approaches combine linearly (EnL) or nonlinearly (EnNL) the forecasting values obtained from all the single automatic methods by considering each model component’s weight. In this work, four electrical time series with different characteristics are examined, to demonstrate the effectiveness and applicability of the proposed ensemble approach—the model performances are compared based on root mean square error (RMSE) and absolute percentage errors (MAPEs). The experimental results show that compared to the existing average weighted ensemble approach, the proposed nonlinear weighted ensemble approach successfully reduces the RMSE and MAPE of the testing data by between 28% and 82%.