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Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system

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  • Fu, Guoyin

Abstract

Due to the high energy consumption in buildings, cooling load forecasting plays a crucial role in the planning, control and operation of heating, ventilating and air-conditioning systems. However, cooling load data series always exhibit nonlinear and dynamic features, making it very difficult to be forecasted accurately. Therefore, a novel deep learning based hybrid approach is originally proposed in this paper for deterministic cooling load forecasting with high accuracy. The approach is a hybrid of empirical mode decomposition, deep belief network and ensemble technique. Empirical mode decomposition is applied to decompose the original cooling load data series into several components with better outliers and behaviors. The hidden nonlinear features and high-level invariant structures in data are effectively extracted by layer-wise pre-training based deep belief network. In addition, ensemble technique is introduced and properly designed to mitigate the impact of uncertainties, i.e., model uncertainty and data noise, on forecasting accuracy. Case studies using real cooling load data collected from Shenzhen and Hong Kong have been implemented. The numerical results demonstrate that the proposed forecasting approach exhibits competitive performance when compared to the prediction algorithms of the state of the art. It is therefore convinced that the proposed approach has a high potential for improving the operating performance in energy systems.

Suggested Citation

  • Fu, Guoyin, 2018. "Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system," Energy, Elsevier, vol. 148(C), pages 269-282.
    • Handle: RePEc:eee:energy:v:148:y:2018:i:c:p:269-282
    DOI: 10.1016/j.energy.2018.01.180
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    References listed on IDEAS

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    1. Wu, Jianghong & Liu, Chaopeng & Li, Hongqi & Ouyang, Dong & Cheng, Jianhong & Wang, Yuanxia & You, Shaofang, 2017. "Residential air-conditioner usage in China and efficiency standardization," Energy, Elsevier, vol. 119(C), pages 1036-1046.
    2. Hou, Zhijian & Lian, Zhiwei & Yao, Ye & Yuan, Xinjian, 2006. "Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique," Applied Energy, Elsevier, vol. 83(9), pages 1033-1046, September.
    3. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    4. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    5. Liang Xue & Cheng Dai & Lei Wang, 2017. "Development of a General Package for Resolution of Uncertainty-Related Issues in Reservoir Engineering," Energies, MDPI, vol. 10(2), pages 1-16, February.
    6. Nowotarski, Jakub & Liu, Bidong & Weron, Rafał & Hong, Tao, 2016. "Improving short term load forecast accuracy via combining sister forecasts," Energy, Elsevier, vol. 98(C), pages 40-49.
    7. Ürge-Vorsatz, Diana & Cabeza, Luisa F. & Serrano, Susana & Barreneche, Camila & Petrichenko, Ksenia, 2015. "Heating and cooling energy trends and drivers in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 85-98.
    8. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    9. Ji, Ying & Xu, Peng & Duan, Pengfei & Lu, Xing, 2016. "Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data," Applied Energy, Elsevier, vol. 169(C), pages 309-323.
    10. Linares-Rodriguez, Alvaro & Quesada-Ruiz, Samuel & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2015. "An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery," Energy, Elsevier, vol. 91(C), pages 264-273.
    11. He, Kaijian & Yu, Lean & Lai, Kin Keung, 2012. "Crude oil price analysis and forecasting using wavelet decomposed ensemble model," Energy, Elsevier, vol. 46(1), pages 564-574.
    12. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
    13. Dedinec, Aleksandra & Filiposka, Sonja & Dedinec, Aleksandar & Kocarev, Ljupco, 2016. "Deep belief network based electricity load forecasting: An analysis of Macedonian case," Energy, Elsevier, vol. 115(P3), pages 1688-1700.
    14. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
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