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Improving the prediction of heating energy consumed at residential buildings using a combination of support vector regression and meta-heuristic algorithms

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  • Khajavi, Hamed
  • Rastgoo, Amir

Abstract

The growing population has caused to increase in energy demand worldwide. Since significant energy consumption in the residential building sector is assigned to the heating. Thus, forecasting consumed energy is effective for planning and providing future demand. However, the prediction of the consumed energy of heating systems in residential buildings due to various variables is complex. This study presents a model based on machine learning to improve the prediction of heating energy consumption in residential buildings. In this regard, Support Vector Regression (SVR) was selected as the central core of problem-solving. Then, using the hybridization technique, SVR was combined with six meta-heuristic algorithms to tune and optimize the hyper-parameters of the SVR algorithm. Finally, the proposed method's performance and accuracy compared to hybrid models by conducting a case study were investigated. The results show that the proposed method can accurately predict the heating energy consumed in residential buildings. Also, it is shown that the hybrid SVR- Battle Royale Optimization model had the best performance among all investigated hybrid models. For example, the R2 values of this model are equal to 0.999386 and 0.998898 for the train and test datasets, which are the highest values among all models.

Suggested Citation

  • Khajavi, Hamed & Rastgoo, Amir, 2023. "Improving the prediction of heating energy consumed at residential buildings using a combination of support vector regression and meta-heuristic algorithms," Energy, Elsevier, vol. 272(C).
    • Handle: RePEc:eee:energy:v:272:y:2023:i:c:s0360544223004632
    DOI: 10.1016/j.energy.2023.127069
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    References listed on IDEAS

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    Cited by:

    1. Bian, Jianxiao & Wang, Jiarui & Yece, Qian, 2024. "A novel study on power consumption of an HVAC system using CatBoost and AdaBoost algorithms combined with the metaheuristic algorithms," Energy, Elsevier, vol. 302(C).
    2. Afzal, Sadegh & Ziapour, Behrooz M. & Shokri, Afshar & Shakibi, Hamid & Sobhani, Behnam, 2023. "Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms," Energy, Elsevier, vol. 282(C).
    3. 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).
    4. 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.
    5. Li, Xuetao & Wang, Ziwei & Yang, Chengying & Bozkurt, Ayhan, 2024. "An advanced framework for net electricity consumption prediction: Incorporating novel machine learning models and optimization algorithms," Energy, Elsevier, vol. 296(C).

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