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Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower

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  • Lu, Hongfang
  • Cheng, Feifei
  • Ma, Xin
  • Hu, Gang

Abstract

Accurate prediction of building energy consumption is crucial for building energy management. However, the building energy consumption is affected by many factors and shows obvious nonlinear characteristics in the time series, which is difficult to predict. In this work, a novel hybrid model is proposed for predicting short-term building energy consumption. In this model, the raw data is decomposed into multiple smooth datasets using complete ensemble empirical mode decomposition with adaptive noise, and the building energy consumption is predicted by the traditional extreme gradient boosting. Taking the daily energy consumption of the City of Bloomington Intake Tower as the simulation object, the results show that the mean absolute percentage error of the proposed model is 4.85%, which is much lower than that of five benchmark models. The proposed model is also applied to the prediction of other parameters related to the energy consumption of the intake tower, and shows good prediction performance. Moreover, the influences of the sliding window length and data attributes on the prediction results are also discussed.

Suggested Citation

  • Lu, Hongfang & Cheng, Feifei & Ma, Xin & Hu, Gang, 2020. "Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower," Energy, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:energy:v:203:y:2020:i:c:s036054422030863x
    DOI: 10.1016/j.energy.2020.117756
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    4. Luca Di Persio & Nicola Fraccarolo, 2023. "Energy Consumption Forecasts by Gradient Boosting Regression Trees," Mathematics, MDPI, vol. 11(5), pages 1-17, February.
    5. Yayuan Feng & Youxian Huang & Haifeng Shang & Junwei Lou & Ala deen Knefaty & Jian Yao & Rongyue Zheng, 2022. "Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression," Energies, MDPI, vol. 15(13), pages 1-19, June.
    6. Chen, Siliang & Ge, Wei & Liang, Xinbin & Jin, Xinqiao & Du, Zhimin, 2024. "Lifelong learning with deep conditional generative replay for dynamic and adaptive modeling towards net zero emissions target in building energy system," Applied Energy, Elsevier, vol. 353(PB).
    7. Chou, Jui-Sheng & Truong, Dinh-Nhat & Kuo, Ching-Chiun, 2021. "Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning," Energy, Elsevier, vol. 224(C).
    8. Palanisamy Manigandan & MD Shabbir Alam & Majed Alharthi & Uzma Khan & Kuppusamy Alagirisamy & Duraisamy Pachiyappan & Abdul Rehman, 2021. "Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models," Energies, MDPI, vol. 14(19), pages 1-17, September.
    9. Li, Guannan & Wu, Yubei & Yoon, Sungmin & Fang, Xi, 2024. "Comprehensive transferability assessment of short-term cross-building-energy prediction using deep adversarial network transfer learning," Energy, Elsevier, vol. 299(C).
    10. Xin Ma & Yubin Cai & Hong Yuan & Yanqiao Deng, 2023. "Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States," Sustainability, MDPI, vol. 15(9), pages 1-26, April.
    11. Mengkun Liang & Renjing Guo & Hongyu Li & Jiaqi Wu & Xiangdong Sun, 2023. "T-LGBKS: An Interpretable Machine Learning Framework for Electricity Consumption Forecasting," Energies, MDPI, vol. 16(11), pages 1-27, May.
    12. Li, Guannan & Li, Fan & Ahmad, Tanveer & Liu, Jiangyan & Li, Tao & Fang, Xi & Wu, Yubei, 2022. "Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions," Energy, Elsevier, vol. 259(C).
    13. Fan, Cheng & Lei, Yutian & Sun, Yongjun & Piscitelli, Marco Savino & Chiosa, Roberto & Capozzoli, Alfonso, 2022. "Data-centric or algorithm-centric: Exploiting the performance of transfer learning for improving building energy predictions in data-scarce context," Energy, Elsevier, vol. 240(C).
    14. Salimian Rizi, Behzad & Pavlak, Gregory & Cushing, Vincent & Heidarinejad, Mohammad, 2023. "Predicting uncertainty of a chiller plant power consumption using quantile random forest: A commercial building case study," Energy, Elsevier, vol. 283(C).
    15. Lu, Hongfang & Ma, Xin & Ma, Minda, 2021. "A hybrid multi-objective optimizer-based model for daily electricity demand prediction considering COVID-19," Energy, Elsevier, vol. 219(C).

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