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Predicting the energy consumption in buildings using the optimized support vector regression model

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  • Cai, Wei
  • Wen, Xiaodong
  • Li, Chaoen
  • Shao, Jingjing
  • Xu, Jianguo

Abstract

One of the most significant axes of regional, national, and worldwide energy policy is energy efficiency in building design. In particular, the energy efficiency of HVAC systems is of paramount importance. They provide energy for both residential and commercial sectors. As a result, assessing building energy usage is a critical step in optimizing building energy consumption. The impact of eight input factors on the two output variables, heating and cooling loads, for residential structures was explored in this study. For this purpose, the SVR-supervised machine learning algorithm was used. Despite its advantages, such as predictive accuracy and robustness, this method suffers from the fact that there is no specific rule for fitting its parameters. Therefore, six meta-heuristic optimization algorithms were investigated, and the strongest algorithm for optimal parameter fitting for the SVR model was presented. The correlation and error parameters analysis showed that the hybrid model SVR-AEO has the best performance in simulating residential buildings' heating and cooling loads. According to the obtained results, the value of R2 for the cooling and heating loads prediction in the training data is obtained to be 0.9975 and 0.99955, respectively.

Suggested Citation

  • Cai, Wei & Wen, Xiaodong & Li, Chaoen & Shao, Jingjing & Xu, Jianguo, 2023. "Predicting the energy consumption in buildings using the optimized support vector regression model," Energy, Elsevier, vol. 273(C).
    • Handle: RePEc:eee:energy:v:273:y:2023:i:c:s0360544223005820
    DOI: 10.1016/j.energy.2023.127188
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    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. Heiselberg, Per & Brohus, Henrik & Hesselholt, Allan & Rasmussen, Henrik & Seinre, Erkki & Thomas, Sara, 2009. "Application of sensitivity analysis in design of sustainable buildings," Renewable Energy, Elsevier, vol. 34(9), pages 2030-2036.
    3. Farzad Hamrang & Afshar Shokri & S. M. Seyed Mahmoudi & Biuk Ehghaghi & Marc A. Rosen, 2020. "Performance Analysis of a New Electricity and Freshwater Production System Based on an Integrated Gasification Combined Cycle and Multi-Effect Desalination," Sustainability, MDPI, vol. 12(19), pages 1-29, September.
    4. Shakibi, Hamid & Shokri, Afshar & Assareh, Ehsanolah & Yari, Mortaza & Lee, Moonyong, 2023. "Using machine learning approaches to model and optimize a combined solar/natural gas-based power and freshwater cogeneration system," Applied Energy, Elsevier, vol. 333(C).
    5. Amirmasood Nakhaee Sharif & Sanaz Keshavarz Saleh & Sadegh Afzal & Niloofar Shoja Razavi & Mozhdeh Fadaei Nasab & Samireh Kadaei & Chun Wei, 2022. "Evaluating and Identifying Climatic Design Features in Traditional Iranian Architecture for Energy Saving (Case Study of Residential Architecture in Northwest of Iran)," Complexity, Hindawi, vol. 2022, pages 1-12, September.
    6. Kialashaki, Arash & Reisel, John R., 2013. "Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks," Applied Energy, Elsevier, vol. 108(C), pages 271-280.
    7. Li, Cheng & Hong, Tianzhen & Yan, Da, 2014. "An insight into actual energy use and its drivers in high-performance buildings," Applied Energy, Elsevier, vol. 131(C), pages 394-410.
    8. 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.
    9. Pan, Yue & Zhang, Limao, 2020. "Data-driven estimation of building energy consumption with multi-source heterogeneous data," Applied Energy, Elsevier, vol. 268(C).
    10. Pacheco, R. & Ordóñez, J. & Martínez, G., 2012. "Energy efficient design of building: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3559-3573.
    11. Hemsath, Timothy L. & Alagheband Bandhosseini, Kaveh, 2015. "Sensitivity analysis evaluating basic building geometry's effect on energy use," Renewable Energy, Elsevier, vol. 76(C), pages 526-538.
    12. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
    13. Beccali, Marco & Ciulla, Giuseppina & Lo Brano, Valerio & Galatioto, Alessandra & Bonomolo, Marina, 2017. "Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy," Energy, Elsevier, vol. 137(C), pages 1201-1218.
    14. Azizi, Saeid & Shakibi, Hamid & Shokri, Afshar & Chitsaz, Ata & Yari, Mortaza, 2023. "Multi-aspect analysis and RSM-based optimization of a novel dual-source electricity and cooling cogeneration system," Applied Energy, Elsevier, vol. 332(C).
    15. Kheiri, Farshad, 2018. "A review on optimization methods applied in energy-efficient building geometry and envelope design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 92(C), pages 897-920.
    16. Aydinalp-Koksal, Merih & Ugursal, V. Ismet, 2008. "Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, Elsevier, vol. 85(4), pages 271-296, April.
    17. Wang, Lan & Lee, Eric W.M. & Yuen, Richard K.K., 2018. "Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach," Applied Energy, Elsevier, vol. 228(C), pages 1740-1753.
    18. Lee, J.W. & Jung, H.J. & Park, J.Y. & Lee, J.B. & Yoon, Y., 2013. "Optimization of building window system in Asian regions by analyzing solar heat gain and daylighting elements," Renewable Energy, Elsevier, vol. 50(C), pages 522-531.
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    5. Kim, Dongsu & Seomun, Gu & Lee, Yongjun & Cho, Heejin & Chin, Kyungil & Kim, Min-Hwi, 2024. "Forecasting building energy demand and on-site power generation for residential buildings using long and short-term memory method with transfer learning," Applied Energy, Elsevier, vol. 368(C).
    6. Meng Wang & Junqi Yu & Meng Zhou & Wei Quan & Renyin Cheng, 2023. "Joint Forecasting Model for the Hourly Cooling Load and Fluctuation Range of a Large Public Building Based on GA-SVM and IG-SVM," Sustainability, MDPI, vol. 15(24), pages 1-23, December.
    7. Søndergaard, Henrik Alexander Nissen & Shaker, Hamid Reza & Jørgensen, Bo Nørregaard, 2024. "Contextual operational energy performance indexing of district heating consumers," Energy, Elsevier, vol. 302(C).

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