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Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building

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  • Ding, Zhikun
  • Chen, Weilin
  • Hu, Ting
  • Xu, Xiaoxiao

Abstract

The prediction of building energy consumption plays a crucial role in building energy management and conservation because it contributes to effective building operation, energy efficiency evaluation, fault detection and diagnosis, and demand side management. Although a large number of energy prediction methods have been proposed, each method has its pros and cons and still exhibits the potential to be improved. This study proposes an evolutionary double attention-based long short- term memory model and introduces binary features by using feature combination. The proposed model is adopted to analyse the building energy consumption data of a green building in Shenzhen, China. The prediction performance of the proposed hybrid model measured via root-mean-square-error and mean absolute error are 4.02 and 2.87 respectively, which are evidently better than those of the base models. Results also show that an attention mechanism can improve the efficiency of the long short-term memory algorithm with which the model uses the input time series data. Meanwhile, binary features exert a significant effect on energy consumption. The proposed model is valuable to researchers and practitioners. It helps researchers apply artificial intelligence-based methods to building energy prediction from the perspective of paying selective attention to input data. Practitioners will benefit from developing accurate diagnosis of building energy efficiency and decision support for building retrofitting.

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  • Ding, Zhikun & Chen, Weilin & Hu, Ting & Xu, Xiaoxiao, 2021. "Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building," Applied Energy, Elsevier, vol. 288(C).
    • Handle: RePEc:eee:appene:v:288:y:2021:i:c:s0306261921001902
    DOI: 10.1016/j.apenergy.2021.116660
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    as
    1. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
    2. 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.
    3. 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).
    4. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    5. Wei, Shuangyu & Tien, Paige Wenbin & Calautit, John Kaiser & Wu, Yupeng & Boukhanouf, Rabah, 2020. "Vision-based detection and prediction of equipment heat gains in commercial office buildings using a deep learning method," Applied Energy, Elsevier, vol. 277(C).
    6. Ramedani, Zeynab & Omid, Mahmoud & Keyhani, Alireza & Shamshirband, Shahaboddin & Khoshnevisan, Benyamin, 2014. "Potential of radial basis function based support vector regression for global solar radiation prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1005-1011.
    7. Jihoon Moon & Yongsung Kim & Minjae Son & Eenjun Hwang, 2018. "Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron," Energies, MDPI, vol. 11(12), pages 1-20, November.
    8. Seon Hyeog Kim & Gyul Lee & Gu-Young Kwon & Do-In Kim & Yong-June Shin, 2018. "Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting," Energies, MDPI, vol. 11(12), pages 1-17, December.
    9. Khuram Pervez Amber & Muhammad Waqar Aslam & Anzar Mahmood & Anila Kousar & Muhammad Yamin Younis & Bilal Akbar & Ghulam Qadar Chaudhary & Syed Kashif Hussain, 2017. "Energy Consumption Forecasting for University Sector Buildings," Energies, MDPI, vol. 10(10), pages 1-18, October.
    10. Chalal, Moulay Larbi & Benachir, Medjdoub & White, Michael & Shrahily, Raid, 2016. "Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 761-776.
    11. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    12. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    13. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    14. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    15. Keles, Dogan & Scelle, Jonathan & Paraschiv, Florentina & Fichtner, Wolf, 2016. "Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks," Applied Energy, Elsevier, vol. 162(C), pages 218-230.
    16. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    17. 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.
    18. Kamal, Rajeev & Moloney, Francesca & Wickramaratne, Chatura & Narasimhan, Arunkumar & Goswami, D.Y., 2019. "Strategic control and cost optimization of thermal energy storage in buildings using EnergyPlus," Applied Energy, Elsevier, vol. 246(C), pages 77-90.
    19. Fan, Cheng & Wang, Jiayuan & Gang, Wenjie & Li, Shenghan, 2019. "Assessment of deep recurrent neural network-based strategies for short-term building energy predictions," Applied Energy, Elsevier, vol. 236(C), pages 700-710.
    20. Moazami, Amin & Nik, Vahid M. & Carlucci, Salvatore & Geving, Stig, 2019. "Impacts of future weather data typology on building energy performance – Investigating long-term patterns of climate change and extreme weather conditions," Applied Energy, Elsevier, vol. 238(C), pages 696-720.
    21. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2020. "Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting," Energies, MDPI, vol. 13(2), pages 1-21, January.
    22. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2002. "Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks," Applied Energy, Elsevier, vol. 71(2), pages 87-110, February.
    23. Tan, Zhongfu & Zhang, Jinliang & Wang, Jianhui & Xu, Jun, 2010. "Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models," Applied Energy, Elsevier, vol. 87(11), pages 3606-3610, November.
    24. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
    25. Soteris A. Kalogirou, 2006. "Artificial neural networks in energy applications in buildings," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 1(3), pages 201-216, July.
    26. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2019. "Data fusion in predicting internal heat gains for office buildings through a deep learning approach," Applied Energy, Elsevier, vol. 240(C), pages 386-398.
    27. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    28. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
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