A Statistical Framework for District Energy Long-term Electric Load Forecasting
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916595256786944 |
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| author | Royal, Emily Bandyopadhyay, Soutir Newman, Alexandra Huang, Qiuhua Tabares-Velasco, Paulo Cesar |
| author_facet | Royal, Emily Bandyopadhyay, Soutir Newman, Alexandra Huang, Qiuhua Tabares-Velasco, Paulo Cesar |
| contents | 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%. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_01531 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Statistical Framework for District Energy Long-term Electric Load Forecasting Royal, Emily Bandyopadhyay, Soutir Newman, Alexandra Huang, Qiuhua Tabares-Velasco, Paulo Cesar Applications 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%. |
| title | A Statistical Framework for District Energy Long-term Electric Load Forecasting |
| topic | Applications |
| url | https://arxiv.org/abs/2502.01531 |