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Development of an Improved Model to Predict Building Thermal Energy Consumption by Utilizing Feature Selection

Author

Listed:
  • Jihoon Jang

    (Department of Architectural Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Joosang Lee

    (Department of Architectural Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Eunjo Son

    (Department of Architectural Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Kyungyong Park

    (Department of Architectural Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Gahee Kim

    (Department of Architectural Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Jee Hang Lee

    (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea)

  • Seung-Bok Leigh

    (Department of Architectural Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

Abstract

Humans spend approximately 90% of the daytime in buildings, and greenhouse gases (GHGs) emitted by buildings account for approximately 20% of total GHG emissions. As the energy consumed during building operation from a building life-cycle perspective amounts to approximately 70–90% of the total energy, it is essential to accurately predict the energy consumption of buildings for their efficient operation. This study aims to optimize a model for predicting the thermal energy consumption of buildings by (i) first extracting major variables through feature selection and deriving significant variables in addition to the collected data and (ii) predicting the thermal energy consumption using a machine learning model. Feature selection using random forest was performed, and 11 out of 17 available data were selected. The accuracy of the prediction model was significantly improved when the hour of day variable was added. The prediction model was constructed using an artificial neural network (ANN), and the improvement in the prediction accuracy was analyzed by comparing different cases of variable combinations. The ANN prediction accuracy was improved by 15% using the feature selection process compared to when all data were used as input data, and 25% coefficient of variation of the root mean square error (CVRMSE) accuracy was achieved.

Suggested Citation

  • Jihoon Jang & Joosang Lee & Eunjo Son & Kyungyong Park & Gahee Kim & Jee Hang Lee & Seung-Bok Leigh, 2019. "Development of an Improved Model to Predict Building Thermal Energy Consumption by Utilizing Feature Selection," Energies, MDPI, vol. 12(21), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4187-:d:282965
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    References listed on IDEAS

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    Cited by:

    1. Jun-Mao Liao & Ming-Jui Chang & Luh-Maan Chang, 2020. "Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques," Energies, MDPI, vol. 13(7), pages 1-22, April.
    2. Zihao Li & Daniel Friedrich & Gareth P. Harrison, 2020. "Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model," Energies, MDPI, vol. 13(4), pages 1-20, February.
    3. Lee-Yong Sung & Jonghoon Ahn, 2020. "Comparative Analyses of Energy Efficiency between on-Demand and Predictive Controls for Buildings’ Indoor Thermal Environment," Energies, MDPI, vol. 13(5), pages 1-15, March.
    4. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    5. Jihoon Jang & Sukumar Natarajan & Joosang Lee & Seung-Bok Leigh, 2022. "Comparative Analysis of Overheating Risk for Typical Dwellings and Passivhaus in the UK," Energies, MDPI, vol. 15(10), pages 1-22, May.

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