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Integrating time series decomposition and multivariable approaches for enhanced cooling energy management

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  • Yu, Fu Wing
  • Ho, Wai Tung
  • Wong, Chak Fung Jeff

Abstract

Predicting electricity consumption for building cooling is a critical challenge for improving sustainability, but existing simulation methods often overlook the interaction between system operations and energy use. This study presents a new approach that improves the accuracy of cooling electricity consumption predictions. The method combines time series decomposition with multivariable modelling, using Seasonal and Trend decomposition using Loess to disaggregate the demand data into trend, seasonal, and residual components. Multivariate regression models are then developed to predict the trend component, integrating a comprehensive set of system operating variables and environmental factors. This innovative modelling framework achieves an exceptional coefficient of determination of 0.9994 in the training set and 0.9905 in the validation set. By using trend component models, the important operating variables are effectively addressed, enabling optimization of the chiller system's operating variables. The minimum electricity consumption is assessed based on boundaries at the 80th, 90th, 95th and 99th percentiles of operating variables. The analysis revealed potential annual savings of 11.178 %–16.401 %, equivalent to 191,805 to 281,427 kW h, with distinct daily and seasonal variability patterns. The novel modelling approach empowers building operators to realize the drivers of energy use, enabling data-driven optimization strategies that enhance sustainability.

Suggested Citation

  • Yu, Fu Wing & Ho, Wai Tung & Wong, Chak Fung Jeff, 2025. "Integrating time series decomposition and multivariable approaches for enhanced cooling energy management," Energy, Elsevier, vol. 318(C).
  • Handle: RePEc:eee:energy:v:318:y:2025:i:c:s0360544225003822
    DOI: 10.1016/j.energy.2025.134740
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    References listed on IDEAS

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    1. Hua, Tian & Yitai, Ma & Minxia, Li & Chuntao, Liu & Li, Zhao, 2010. "The status and development trend of the water chiller energy efficiency standard in China," Energy Policy, Elsevier, vol. 38(11), pages 7497-7503, November.
    2. Zhang, Guiqing & Tian, Chenlu & Li, Chengdong & Zhang, Jun Jason & Zuo, Wangda, 2020. "Accurate forecasting of building energy consumption via a novel ensembled deep learning method considering the cyclic feature," Energy, Elsevier, vol. 201(C).
    3. Zheng, Peijun & Zhou, Heng & Liu, Jiang & Nakanishi, Yosuke, 2023. "Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture," Applied Energy, Elsevier, vol. 349(C).
    4. Yuan, Zhi & Wang, Weiqing & Wang, Haiyun & Mizzi, Scott, 2020. "Combination of cuckoo search and wavelet neural network for midterm building energy forecast," Energy, Elsevier, vol. 202(C).
    5. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    6. Tran, Duc-Hoc & Luong, Duc-Long & Chou, Jui-Sheng, 2020. "Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings," Energy, Elsevier, vol. 191(C).
    7. Huo, Meiqi & Yan, Weijie & Ren, Guoqian & Li, Yu, 2024. "A novel hybrid model based on modal decomposition and error correction for building energy consumption prediction," Energy, Elsevier, vol. 294(C).
    8. Yesilyurt, Hasan & Dokuz, Yesim & Dokuz, Ahmet Sakir, 2024. "Data-driven energy consumption prediction of a university office building using machine learning algorithms," Energy, Elsevier, vol. 310(C).
    9. Fang, Yu & Jia, Chunhong & Wang, Xin & Min, Fan, 2024. "A fusion gas load prediction model with three-way residual error amendment," Energy, Elsevier, vol. 294(C).
    10. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    11. Lu, Hongfang & Cheng, Feifei & Ma, Xin & Hu, Gang, 2020. "Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower," Energy, Elsevier, vol. 203(C).
    12. Vaghefi, A. & Jafari, M.A. & Bisse, Emmanuel & Lu, Y. & Brouwer, J., 2014. "Modeling and forecasting of cooling and electricity load demand," Applied Energy, Elsevier, vol. 136(C), pages 186-196.
    13. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    14. Yang, Xiu'e & Liu, Shuli & Zou, Yuliang & Ji, Wenjie & Zhang, Qunli & Ahmed, Abdullahi & Han, Xiaojing & Shen, Yongliang & Zhang, Shaoliang, 2022. "Energy-saving potential prediction models for large-scale building: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    15. Cai, Mengmeng & Pipattanasomporn, Manisa & Rahman, Saifur, 2019. "Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques," Applied Energy, Elsevier, vol. 236(C), pages 1078-1088.
    16. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    17. Wang, Lan & Lee, Eric W.M. & Yuen, Richard K.K., 2018. "Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach," Applied Energy, Elsevier, vol. 228(C), pages 1740-1753.
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