Domain knowledge decomposition of building energy consumption and a hybrid data-driven model for 24-h ahead predictions

Xinbin Liang, Siliang Chen, Xu Zhu, Xinqiao Jin and Zhimin Du

Applied Energy, 2023, vol. 344, issue C, No S0306261923006086

Abstract: The task of building energy prediction (BEP) is essential to several emerging research domains, including energy management, control optimization and fault detection. An accuracy prediction model contributes significantly to the improvement of building energy efficiency and flexibility. However, most existing prediction models are either based on single time series model or static model, making them perform poorly for long-term predictions. To solve this problem, this paper proposes a hybrid prediction model, which combines the deep ensemble (DE) model and autoregressive (AR) model together. Its motivation comes from the domain knowledge analysis of building energy consumption, where the energy consumption is decomposed into a global part and a local part. The deep ensemble model is adopted to predict its global part, and the AR model is employed to predict its local part. Comprehensive data experiments are conducted based on 50 real buildings across five building types to validate the model performance. The prediction horizon is the 24-h ahead sliding window prediction for one-year energy consumption. The results indicate that the hybrid prediction model outperforms the LSTM, DE-only, and ARIMA-only model, where its relative improvements of CV-RMSE are 28.7%, 35.98% and 18.47%, respectively. The experimental results also reveal that the types of buildings, i.e., office, lodging, will affect the model performance, which is attributed to their different user-behaviors. Based on the experimental results, it is demonstrated that the integration of static model and time series model is robust to the varying of building types and prediction steps, which should be the preferred choice of BEP task.

Keywords: Building energy prediction; Hybrid model; Data-driven method; Deep learning; Time series (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (12)

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DOI: 10.1016/j.apenergy.2023.121244

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