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Short-term building energy model recommendation system: A meta-learning approach


  • Cui, Can
  • Wu, Teresa
  • Hu, Mengqi
  • Weir, Jeffery D.
  • Li, Xiwang


High-fidelity and computationally efficient energy forecasting models for building systems are needed to ensure optimal automatic operation, reduce energy consumption, and improve the building’s resilience capability to power disturbances. Various models have been developed to forecast building energy consumption. However, given buildings have different characteristics and operating conditions, model performance varies. Existing research has mainly taken a trial-and-error approach by developing multiple models and identifying the best performer for a specific building, or presumed one universal model form which is applied on different building cases. To the best of our knowledge, there does not exist a generalized system framework which can recommend appropriate models to forecast the building energy profiles based on building characteristics. To bridge this research gap, we propose a meta-learning based framework, termed Building Energy Model Recommendation System (BEMR). Based on the building’s physical features as well as statistical and time series meta-features extracted from the operational data and energy consumption data, BEMR is able to identify the most appropriate load forecasting model for each unique building. Three sets of experiments on 48 test buildings and one real building were conducted. The first experiment was to test the accuracy of BEMR when the training data and testing data cover the same condition. BEMR correctly identified the best model on 90% of the buildings. The second experiment was to test the robustness of the BEMR when the testing data is only partially covered by the training data. BEMR correctly identified the best model on 83% of the buildings. The third experiment uses a real building case to validate the proposed framework and the result shows promising applicability and extensibility. The experimental results show that BEMR is capable of adapting to a wide variety of building types ranging from a restaurant to a large office, and gives excellent performance in terms of both modeling accuracy and computational efficiency.

Suggested Citation

  • Cui, Can & Wu, Teresa & Hu, Mengqi & Weir, Jeffery D. & Li, Xiwang, 2016. "Short-term building energy model recommendation system: A meta-learning approach," Applied Energy, Elsevier, vol. 172(C), pages 251-263.
  • Handle: RePEc:eee:appene:v:172:y:2016:i:c:p:251-263
    DOI: 10.1016/j.apenergy.2016.03.112

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    2. Chen, Yujiao & Malkawi, Ali & Liu, Zhu & Freeman, Richard Barry & Tong, Zheming, 2016. "Energy Saving Potential of Natural Ventilation in China: The Impact of Ambient Air Pollution," Scholarly Articles 27733689, Harvard University Department of Economics.
    3. 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.
    4. Wei, Yixuan & Xia, Liang & Pan, Song & Wu, Jinshun & Zhang, Xingxing & Han, Mengjie & Zhang, Weiya & Xie, Jingchao & Li, Qingping, 2019. "Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks," Applied Energy, Elsevier, vol. 240(C), pages 276-294.
    5. Tong, Zheming & Chen, Yujiao & Malkawi, Ali & Liu, Zhu & Freeman, Richard B., 2016. "Energy saving potential of natural ventilation in China: The impact of ambient air pollution," Applied Energy, Elsevier, vol. 179(C), pages 660-668.
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    7. Marzouk, Mohamed & Seleem, Noreihan, 2018. "Assessment of existing buildings performance using system dynamics technique," Applied Energy, Elsevier, vol. 211(C), pages 1308-1323.
    8. Iria, José & Soares, Filipe & Matos, Manuel, 2018. "Optimal supply and demand bidding strategy for an aggregator of small prosumers," Applied Energy, Elsevier, vol. 213(C), pages 658-669.
    9. Pino-Mejías, Rafael & Pérez-Fargallo, Alexis & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A., 2017. "Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions," Energy, Elsevier, vol. 118(C), pages 24-36.
    10. Liu, Yang & Wang, Wei & Ghadimi, Noradin, 2017. "Electricity load forecasting by an improved forecast engine for building level consumers," Energy, Elsevier, vol. 139(C), pages 18-30.
    11. Spandagos, Constantinos & Ng, Tze Ling, 2017. "Equivalent full-load hours for assessing climate change impact on building cooling and heating energy consumption in large Asian cities," Applied Energy, Elsevier, vol. 189(C), pages 352-368.
    12. Chen, Yang & Hu, Mengqi & Zhou, Zhi, 2017. "A data-driven analytical approach to enable optimal emerging technologies integration in the co-optimized electricity and ancillary service markets," Energy, Elsevier, vol. 122(C), pages 613-626.
    13. Tong, Zheming & Chen, Yujiao & Malkawi, Ali, 2017. "Estimating natural ventilation potential for high-rise buildings considering boundary layer meteorology," Applied Energy, Elsevier, vol. 193(C), pages 276-286.
    14. 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.
    15. 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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