Analysis and Forecasting of Monthly Electricity Demand Time Series Using Pattern-Based Statistical Methods

This article provides a solution based on statistical methods (ARIMA, ETS, and Prophet) to predict monthly power demand, which approximates the relationship between historical future demand patterns. The energy time series shows seasonal fluctuation cycles, long-term trends, instability, random noise. In order simplify prediction issue, load is represented by an annual cycle pattern, unifies data filters trends. A simulation study performed electricity for 35 European countries confirmed high accuracy of proposed models.