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<p>Accurate power demand prediction is essential for utility operations and building energy management to enable efficient grid operation and proactive control of energy storage systems. In residential buildings, demand patterns are highly variable due to diverse occupant behaviors and usage fluctuations, introducing uncertainty that makes accurate forecasting challenging. However, existing approaches often focus on point predictions that lack demand uncertainty information, limiting the ability to characterize uncertainty to make appropriate operational decisions. One potential approach is to employ an accurate and reliable probabilistic model. In this context, this study develops a probabilistic forecasting framework to capture uncertainties in the applications of predicting power demand at both single- and multi-unit levels. First, a multiple seasonal-trend decomposition using the LOESS algorithm decomposes power consumption data into trend, seasonal, and residual components to mitigate the effects of noise. This procedure enables the predictive model to focus on the meaningful variations that affect demand. Then, we extend the Bayesian additive regression tree technique by incorporating both linear and non-linear components to capture complex relationships and generate probabilistic forecasting. The proposed method is evaluated using real-world datasets from residential buildings, focusing on both overall and peak demand scenarios. The power demand data exhibit high variability and complexity, representing diverse occupant behaviors and usage patterns. Results show that the proposed method achieves high accuracy for both mean and probabilistic predictions. It achieves an average CV-RMSE of 15.48% for overall predictions and 16.12% for peak demands, along with PICP values of 97.91% and 97.94%, respectively. These results significantly exceed ASHRAE requirements and outperform other benchmark methods. The findings show that the proposed method can support risk-informed decision-making and enhance energy efficiency.</p>

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A novel MSTL-BART method for probabilistic power demand prediction for residential buildings

  • 期刊论文

  • 发表日期：

    2026年4月1日

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  • Bowen Yang,
  • Omid Ardakanian,
  • Mustafa Gül,
  • Yuxiang Chen

摘要

Accurate power demand prediction is essential for utility operations and building energy management to enable efficient grid operation and proactive control of energy storage systems. In residential buildings, demand patterns are highly variable due to diverse occupant behaviors and usage fluctuations, introducing uncertainty that makes accurate forecasting challenging. However, existing approaches often focus on point predictions that lack demand uncertainty information, limiting the ability to characterize uncertainty to make appropriate operational decisions. One potential approach is to employ an accurate and reliable probabilistic model. In this context, this study develops a probabilistic forecasting framework to capture uncertainties in the applications of predicting power demand at both single- and multi-unit levels. First, a multiple seasonal-trend decomposition using the LOESS algorithm decomposes power consumption data into trend, seasonal, and residual components to mitigate the effects of noise. This procedure enables the predictive model to focus on the meaningful variations that affect demand. Then, we extend the Bayesian additive regression tree technique by incorporating both linear and non-linear components to capture complex relationships and generate probabilistic forecasting. The proposed method is evaluated using real-world datasets from residential buildings, focusing on both overall and peak demand scenarios. The power demand data exhibit high variability and complexity, representing diverse occupant behaviors and usage patterns. Results show that the proposed method achieves high accuracy for both mean and probabilistic predictions. It achieves an average CV-RMSE of 15.48% for overall predictions and 16.12% for peak demands, along with PICP values of 97.91% and 97.94%, respectively. These results significantly exceed ASHRAE requirements and outperform other benchmark methods. The findings show that the proposed method can support risk-informed decision-making and enhance energy efficiency.