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Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms

Author

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  • Fateme Dinmohammadi

    (School of Computing and Engineering, University of West London, London W5 5RF, UK
    The Bartlett Center of Advanced Spatial Analysis (CASA), University College London (UCL), Gower Street, London WC1E 6BTL, UK)

  • Yuxuan Han

    (The Bartlett Center of Advanced Spatial Analysis (CASA), University College London (UCL), Gower Street, London WC1E 6BTL, UK)

  • Mahmood Shafiee

    (School of Mechanical Engineering Sciences, University of Surrey, Guildford GU2 7XH, UK)

Abstract

The share of residential building energy consumption in global energy consumption has rapidly increased after the COVID-19 crisis. The accurate prediction of energy consumption under different indoor and outdoor conditions is an essential step towards improving energy efficiency and reducing carbon footprints in the residential building sector. In this paper, a PSO-optimized random forest classification algorithm is proposed to identify the most important factors contributing to residential heating energy consumption. A self-organizing map (SOM) approach is applied for feature dimensionality reduction, and an ensemble classification model based on the stacking method is trained on the dimensionality-reduced data. The results show that the stacking model outperforms the other models with an accuracy of 95.4% in energy consumption prediction. Finally, a causal inference method is introduced in addition to Shapley Additive Explanation (SHAP) to explore and analyze the factors influencing energy consumption. A clear causal relationship between water pipe temperature changes, air temperature, and building energy consumption is found, compensating for the neglect of temperature in the SHAP analysis. The findings of this research can help residential building owners/managers make more informed decisions around the selection of efficient heating management systems to save on energy bills.

Suggested Citation

  • Fateme Dinmohammadi & Yuxuan Han & Mahmood Shafiee, 2023. "Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms," Energies, MDPI, vol. 16(9), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3748-:d:1134633
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    References listed on IDEAS

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    Cited by:

    1. Adam Slowik & Dorin Moldovan, 2024. "Multi-Objective Plum Tree Algorithm and Machine Learning for Heating and Cooling Load Prediction," Energies, MDPI, vol. 17(12), pages 1-23, June.
    2. Donatien Koulla Moulla & David Attipoe & Ernest Mnkandla & Alain Abran, 2024. "Predictive Model of Energy Consumption Using Machine Learning: A Case Study of Residential Buildings in South Africa," Sustainability, MDPI, vol. 16(11), pages 1-18, May.
    3. Suli Zhang & Yiting Chang & Hui Li & Guanghao You, 2024. "Research on Building Energy Consumption Prediction Based on Improved PSO Fusion LSSVM Model," Energies, MDPI, vol. 17(17), pages 1-17, August.
    4. Xu, Weiyan & Tu, Jielei & Xu, Ning & Liu, Zuming, 2024. "Predicting daily heating energy consumption in residential buildings through integration of random forest model and meta-heuristic algorithms," Energy, Elsevier, vol. 301(C).
    5. Sami Kabir & Mohammad Shahadat Hossain & Karl Andersson, 2024. "An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of Buildings," Energies, MDPI, vol. 17(8), pages 1-18, April.
    6. Khaled Bawaneh & Samir Das & Md. Rasheduzzaman, 2024. "Energy Consumption Analysis and Characterization of the Residential Sector in the US towards Sustainable Development," Energies, MDPI, vol. 17(11), pages 1-24, June.
    7. Jonghoon Kim & Soo-Young Moon & Daehee Jang, 2023. "Spatial Model for Energy Consumption of LEED-Certified Buildings," Sustainability, MDPI, vol. 15(22), pages 1-15, November.
    8. Marian B. Gorzałczany & Filip Rudziński, 2024. "Energy Consumption Prediction in Residential Buildings—An Accurate and Interpretable Machine Learning Approach Combining Fuzzy Systems with Evolutionary Optimization," Energies, MDPI, vol. 17(13), pages 1-24, July.
    9. Luca Gugliermetti & Fabrizio Cumo & Sofia Agostinelli, 2024. "A Future Direction of Machine Learning for Building Energy Management: Interpretable Models," Energies, MDPI, vol. 17(3), pages 1-27, February.

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