Mid-Term Forecasting of Electricity Demand with Multiple Seasonal Cycles

The increasing penetration of intermittent renewable energy sources such as solar and wind has heightened the need for electricity demand forecasts that are both granular and available well in advance. While existing studies focus either on short-term hourly forecasting or on mid- to long-term forecasting at aggregated levels, the problem of forecasting hourly electricity demand a year in advance remains largely unexplored. This paper proposes a parsimonious, univariate mid-term load forecasting (MTLF) model based on centered moving averages that captures three levels of seasonality typically present in hourly electricity demand, i.e., hour of the day, hour of the week, and hour of the year. The proposed approach requires no parameter initialization, and is computationally simple and easy to interpret, making it well suited for practical decision-making. Using real-life demand data from six European countries with diverse demographic and economic characteristics, we compare the performance of the proposed model with extensions of the Holt-Winters and Holt-Winters-Taylor exponential smoothing methods. The results show that the proposed model consistently outperforms the benchmark methods in terms of accuracy and robustness, achieving mean absolute percentage errors ranging from 3.27% to 5.52%. Overall, the paper highlights the value of granular mid-term forecasting in improving renewable energy utilization, supporting capacity planning, and informing regulatory decisions in modern electricity systems.