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Machine-learning based hybrid demand-side controller for high-rise office buildings with high energy flexibilities

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  • Zhou, Yuekuan
  • Zheng, Siqian

Abstract

The accurate demand prediction with high efficiency and advanced demand-side controller are essential for the enhancement of energy flexibility provided by buildings, whereas the current literature fails to present the mechanism on modelling development and demand-side control. This paper aims to deal with the complexity of building demand prediction with supervised machine learning method, including the multiple linear regression, the support vector regression and the backpropagation neural network. The regularization, adding the sum of the weights to the learning function, is utilized to improve the training speed and to solve the overfitting by eliminating the unnecessary connections with small weights. The configuration of the artificial neural network was presented, and sensitivity analysis has been conducted on the learning performance regarding different training times. Energy flexibilities of sophisticated building energy systems (including renewable system, electric and thermal demands and building services systems) were quantitatively characterised with a series of quantifiable indicators. Moreover, several advanced controllers have been developed and contrasted, in regard to the flexibility utilisation of building energy systems. Results showed that, the developed hybrid controller with short-term prediction through the cross-entropy function is more technically competitive than other controllers. With the implementation of the developed hybrid controller, the peak power of the grid importation can be reduced from 500.3 to 195 kW by 61%. This study formulates a data-driven model with an advanced machine learning algorithm for the accurate building demand prediction and a hybrid advanced controller with short-term prediction for the energy management, which are critical for the promotion of energy flexible buildings.

Suggested Citation

  • Zhou, Yuekuan & Zheng, Siqian, 2020. "Machine-learning based hybrid demand-side controller for high-rise office buildings with high energy flexibilities," Applied Energy, Elsevier, vol. 262(C).
    • Handle: RePEc:eee:appene:v:262:y:2020:i:c:s0306261919321038
    DOI: 10.1016/j.apenergy.2019.114416
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    as
    1. Le Dréau, J. & Heiselberg, P., 2016. "Energy flexibility of residential buildings using short term heat storage in the thermal mass," Energy, Elsevier, vol. 111(C), pages 991-1002.
    2. Brown, Donal & Sorrell, Steve & Kivimaa, Paula, 2019. "Worth the risk? An evaluation of alternative finance mechanisms for residential retrofit," Energy Policy, Elsevier, vol. 128(C), pages 418-430.
    3. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
    4. Drgoňa, Ján & Picard, Damien & Kvasnica, Michal & Helsen, Lieve, 2018. "Approximate model predictive building control via machine learning," Applied Energy, Elsevier, vol. 218(C), pages 199-216.
    5. De Coninck, Roel & Helsen, Lieve, 2016. "Quantification of flexibility in buildings by cost curves – Methodology and application," Applied Energy, Elsevier, vol. 162(C), pages 653-665.
    6. Peng, Yuzhen & Rysanek, Adam & Nagy, Zoltán & Schlüter, Arno, 2018. "Using machine learning techniques for occupancy-prediction-based cooling control in office buildings," Applied Energy, Elsevier, vol. 211(C), pages 1343-1358.
    7. Longhi, Simonetta, 2015. "Residential energy expenditures and the relevance of changes in household circumstances," Energy Economics, Elsevier, vol. 49(C), pages 440-450.
    8. Zhou, Yuekuan & Zheng, Siqian, 2020. "Uncertainty study on thermal and energy performances of a deterministic parameters based optimal aerogel glazing system using machine-learning method," Energy, Elsevier, vol. 193(C).
    9. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine-learning based study on the on-site renewable electrical performance of an optimal hybrid PCMs integrated renewable system with high-level parameters’ uncertainties," Renewable Energy, Elsevier, vol. 151(C), pages 403-418.
    10. 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).
    11. Salkuti, Surender Reddy, 2019. "Day-ahead thermal and renewable power generation scheduling considering uncertainty," Renewable Energy, Elsevier, vol. 131(C), pages 956-965.
    12. Filippini, Massimo & Pachauri, Shonali, 2004. "Elasticities of electricity demand in urban Indian households," Energy Policy, Elsevier, vol. 32(3), pages 429-436, February.
    13. Pallonetto, Fabiano & De Rosa, Mattia & Milano, Federico & Finn, Donal P., 2019. "Demand response algorithms for smart-grid ready residential buildings using machine learning models," Applied Energy, Elsevier, vol. 239(C), pages 1265-1282.
    14. Jing, Rui & Wang, Meng & Zhang, Zhihui & Wang, Xiaonan & Li, Ning & Shah, Nilay & Zhao, Yingru, 2019. "Distributed or centralized? Designing district-level urban energy systems by a hierarchical approach considering demand uncertainties," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    15. Xue, Xue & Wang, Shengwei & Sun, Yongjun & Xiao, Fu, 2014. "An interactive building power demand management strategy for facilitating smart grid optimization," Applied Energy, Elsevier, vol. 116(C), pages 297-310.
    16. Besagni, Giorgio & Borgarello, Marco, 2018. "The determinants of residential energy expenditure in Italy," Energy, Elsevier, vol. 165(PA), pages 369-386.
    17. Junker, Rune Grønborg & Azar, Armin Ghasem & Lopes, Rui Amaral & Lindberg, Karen Byskov & Reynders, Glenn & Relan, Rishi & Madsen, Henrik, 2018. "Characterizing the energy flexibility of buildings and districts," Applied Energy, Elsevier, vol. 225(C), pages 175-182.
    18. Zhang, Sheng & Sun, Yongjun & Cheng, Yong & Huang, Pei & Oladokun, Majeed Olaide & Lin, Zhang, 2018. "Response-surface-model-based system sizing for Nearly/Net zero energy buildings under uncertainty," Applied Energy, Elsevier, vol. 228(C), pages 1020-1031.
    19. Reynders, Glenn & Diriken, Jan & Saelens, Dirk, 2017. "Generic characterization method for energy flexibility: Applied to structural thermal storage in residential buildings," Applied Energy, Elsevier, vol. 198(C), pages 192-202.
    20. Galvin, Ray & Sunikka-Blank, Minna, 2013. "Economic viability in thermal retrofit policies: Learning from ten years of experience in Germany," Energy Policy, Elsevier, vol. 54(C), pages 343-351.
    21. Brounen, Dirk & Kok, Nils & Quigley, John M., 2012. "Residential energy use and conservation: Economics and demographics," European Economic Review, Elsevier, vol. 56(5), pages 931-945.
    22. Lizana, Jesus & Friedrich, Daniel & Renaldi, Renaldi & Chacartegui, Ricardo, 2018. "Energy flexible building through smart demand-side management and latent heat storage," Applied Energy, Elsevier, vol. 230(C), pages 471-485.
    23. Gao, Dian-ce & Sun, Yongjun & Lu, Yuehong, 2015. "A robust demand response control of commercial buildings for smart grid under load prediction uncertainty," Energy, Elsevier, vol. 93(P1), pages 275-283.
    24. Hocine, Amine & Kouaissah, Noureddine & Bettahar, Samir & Benbouziane, Mohamed, 2018. "Optimizing renewable energy portfolios under uncertainty: A multi-segment fuzzy goal programming approach," Renewable Energy, Elsevier, vol. 129(PA), pages 540-552.
    25. Drysdale, Brian & Wu, Jianzhong & Jenkins, Nick, 2015. "Flexible demand in the GB domestic electricity sector in 2030," Applied Energy, Elsevier, vol. 139(C), pages 281-290.
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