Long-Term Multi-Resolution Probabilistic Load Forecasting Using Temporal Hierarchies

Shafie Bahman and Hamidreza Zareipour ()
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Shafie Bahman: Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Hamidreza Zareipour: Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada

Energies, 2025, vol. 18, issue 11, 1-30

Abstract: Accurate long-term electricity load forecasting is critical for energy planning, infrastructure development, and risk management, especially under increasing uncertainty from climate and economic shifts. This study proposes a multi-resolution probabilistic load forecasting framework that leverages temporal hierarchies to generate coherent forecasts at hourly, daily, monthly, and yearly levels. The model integrates climate and economic indicators and employs tailored forecasting techniques at each resolution, including XGBoost and ARIMAX. Initially incoherent forecasts across time scales are reconciled using advanced methods such as Ordinary Least Squares (OLS), Weighted Least Squares with Series Variance Scaling (WLS_V), and Structural Scaling (WLS_S) to ensure consistency. Using historical data from Alberta, Canada, the proposed approach improves the accuracy of deterministic forecasts and enhances the reliability of probabilistic forecasts, particularly when using the OLS reconciliation method. These results highlight the value of temporal hierarchy structures in producing high-resolution long-horizon load forecasts, providing actionable insights for utilities and policymakers involved in long-term energy planning and system optimization.

Keywords: load forecasting; probabilistic forecasts; hierarchical forecasting; temporal aggregation; forecast reconciliation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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