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Accurate forecasting of building energy consumption via a novel ensembled deep learning method considering the cyclic feature

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  • Zhang, Guiqing
  • Tian, Chenlu
  • Li, Chengdong
  • Zhang, Jun Jason
  • Zuo, Wangda

Abstract

Short-term forecasting of building energy consumption (BEC) is significant for building energy reduction and real-time demand response. In this study, we propose a new method to realize half-hourly BEC prediction. In this new method, to fully utilize the existing data features and to further promote the forecasting performance, we divide the BEC data into the stable (cyclic) and stochastic components, and propose a novel hybrid model to model the stable and stochastic components respectively. The cyclic feature (CF) is extracted via the spectrum analysis, while the stochastic component is approximated by a novel Deep Belief Network (DBN) and Extreme Learning Machine (ELM) based ensembled model (DEEM). This novel hybrid model is named DEEM + CF. Furthermore, two real-world BEC experiments are performed to verify the proposed method. Also, to display the superiorities of the proposed DEEM + CF, this model is compared with the DBN, DBN + CF, ELM, ELM + CF, Support Vector Regression (SVR) and SVR + CF. Experimental results indicate that the CF has a great influence on the promotion of forecasting accuracy for approximately 20%, and DEEM + CF performance is the best among the comparative models, with at least 3%, 6%, 10% better accuracy than the DBN + CF, ELM + CF and SVR + CF respectively under the criteria of MAE.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:201:y:2020:i:c:s0360544220306381
    DOI: 10.1016/j.energy.2020.117531
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    References listed on IDEAS

    as
    1. 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.
    2. Alizamir, Meysam & Kim, Sungwon & Kisi, Ozgur & Zounemat-Kermani, Mohammad, 2020. "A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions," Energy, Elsevier, vol. 197(C).
    3. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    4. Huang, Pei & Sun, Yongjun, 2019. "A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level," Energy, Elsevier, vol. 174(C), pages 911-921.
    5. Chambers, Jonathan & Hollmuller, Pierre & Bouvard, Olivia & Schueler, Andreas & Scartezzini, Jean-Louis & Azar, Elie & Patel, Martin K., 2019. "Evaluating the electricity saving potential of electrochromic glazing for cooling and lighting at the scale of the Swiss non-residential national building stock using a Monte Carlo model," Energy, Elsevier, vol. 185(C), pages 136-147.
    6. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine learning-based optimal design of a phase change material integrated renewable system with on-site PV, radiative cooling and hybrid ventilations—study of modelling and application in five clima," Energy, Elsevier, vol. 192(C).
    7. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    8. Chengdong Li & Zixiang Ding & Jianqiang Yi & Yisheng Lv & Guiqing Zhang, 2018. "Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction," Energies, MDPI, vol. 11(1), pages 1-26, January.
    9. Xu, Yuan & Zhang, Mingqing & Ye, Liangliang & Zhu, Qunxiong & Geng, Zhiqiang & He, Yan-Lin & Han, Yongming, 2018. "A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction," Energy, Elsevier, vol. 164(C), pages 137-146.
    10. John Boland, 2020. "Characterising Seasonality of Solar Radiation and Solar Farm Output," Energies, MDPI, vol. 13(2), pages 1-15, January.
    11. Ashouri, Milad & Fung, Benjamin C.M. & Haghighat, Fariborz & Yoshino, Hiroshi, 2020. "Systematic approach to provide building occupants with feedback to reduce energy consumption," Energy, Elsevier, vol. 194(C).
    12. Fan, Cheng & Huang, Gongsheng & Sun, Yongjun, 2018. "A collaborative control optimization of grid-connected net zero energy buildings for performance improvements at building group level," Energy, Elsevier, vol. 164(C), pages 536-549.
    13. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    Full references (including those not matched with items on IDEAS)

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