Accurate peak demand forecasting is critical for power planning as underestimation leads to blackouts and load shedding, while overestimation results in stranded capital and inflated tariffs. Yet common approaches, deterministic extrapolation, machine learning, and time-series models, are ill-suited to small, non-stationary, data-constrained settings typical of developing economies. This paper develops a non-stationary Generalized Extreme Value (GEV) framework for forecasting annual peak electricity demand, in which the location parameter evolves linearly with time to capture systematic trends in extremes. 

Authors: Atikuzzaman Shazeed, Abrar Ahammed Bhuiyan, Khondaker Golam Moazzem
Publication Period: July 2026