A statistical framework for district energy long-term electric load forecasting

Emily Royal, Soutir Bandyopadhyay, Alexandra Newman, Qiuhua Huang and Paulo Cesar Tabares-Velasco

Applied Energy, 2025, vol. 384, issue C, No S0306261925001758

Abstract: An accurate forecast of electric demand is essential for the optimal design of a generation system. For district installations, the projected lifespan may extend one or two decades. The reliance on a single-year forecast, combined with a fixed load growth rate, is the current industry standard, but does not support a multi-decade investment. Existing work on long-term forecasting focuses on annual growth rate and/or uses time resolution that is coarser than hourly. To address the gap, we propose multiple statistical forecast models, verified over as long as an 11-year horizon. Combining demand data, weather data, and occupancy trends results in a hybrid statistical model, i.e., generalized additive model (GAM) with a seasonal autoregressive integrated moving average (SARIMA) of the GAM residuals, a multiple linear regression (MLR) model, and a GAM with ARIMA errors model. We evaluate accuracy based on: (i) annual growth rates of monthly peak loads; (ii) annual growth rates of overall energy consumption; (iii) preservation of daily, weekly, and month-to-month trends that occur within each year, known as the “seasonality” of the data; and, (iv) realistic representation of demand for a full range of weather and occupancy conditions. For example, the models yield an 11-year forecast from a one-year training data set with a normalized root mean square error of 9.091%, a six-year forecast from a one-year training data set with a normalized root mean square error of 8.949%, and a one-year forecast from a 1.2-year training data set with a normalized root mean square error of 6.765%.

Keywords: Electric demand forecasting; District energy; Renewable energy technologies; Long-term load forecasting; Generalized additive model; SARIMA (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125445

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