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A novel optimization method combining metaheuristics and machine learning for daily optimal operations in building energy and storage systems

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  • Ikeda, Shintaro
  • Nagai, Tatsuo

Abstract

In recent years, research on operational optimization of buildings and regional energy systems has been actively conducted. There are several groups that utilized linear approximations, considered nonlinearity, conducted scenario-based research, and used an optimization algorithm to find an optimum solution. In terms of real-world implementation in buildings, the nonlinearity of machine characteristics should be considered within practical computation time because linearization incurs modeling costs, and computational resources are limited. Hence, the authors propose a hybrid algorithm that consists of metaheuristics and machine learning for optimizing daily operating schedules in building energy systems. The deep neural network machine learning technique was used to predict optimal operations of integrated cooling tower systems, and metaheuristics were used to optimize the operation of the other components. The proposed method may reduce daily operating costs by more than 13.4%. In addition, the integrated cooling tower system evaluated in this study reduced cost and energy requirements compared to an individual cooling tower system.

Suggested Citation

  • Ikeda, Shintaro & Nagai, Tatsuo, 2021. "A novel optimization method combining metaheuristics and machine learning for daily optimal operations in building energy and storage systems," Applied Energy, Elsevier, vol. 289(C).
    • Handle: RePEc:eee:appene:v:289:y:2021:i:c:s0306261921002361
    DOI: 10.1016/j.apenergy.2021.116716
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    Citations

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    Cited by:

    1. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    2. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    3. Yin, Sihua & Yang, Haidong & Xu, Kangkang & Zhu, Chengjiu & Zhang, Shaqing & Liu, Guosheng, 2022. "Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty," Applied Energy, Elsevier, vol. 307(C).
    4. Ghafoori, Mahdi & Abdallah, Moatassem & Kim, Serena, 2023. "Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system," Applied Energy, Elsevier, vol. 340(C).
    5. Sulaiman, Mohd Herwan & Mustaffa, Zuriani, 2024. "Chiller energy prediction in commercial building: A metaheuristic-Enhanced deep learning approach," Energy, Elsevier, vol. 297(C).
    6. Shakeri, Alireza & Asadbagi, Poorya & Babamiri Naamrudi, Arash, 2024. "Techno-economic, techno-environmental assessments, and deep learning optimization of an integrated system for CO2 capturing from a gas turbine: Tehran case study," Energy, Elsevier, vol. 306(C).
    7. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    8. Qin, Chun & Zhao, Jun & Chen, Long & Liu, Ying & Wang, Wei, 2022. "An adaptive piecewise linearized weighted directed graph for the modeling and operational optimization of integrated energy systems," Energy, Elsevier, vol. 244(PA).
    9. Ibrahim Al-Shourbaji & Pramod H. Kachare & Samah Alshathri & Salahaldeen Duraibi & Bushra Elnaim & Mohamed Abd Elaziz, 2022. "An Efficient Parallel Reptile Search Algorithm and Snake Optimizer Approach for Feature Selection," Mathematics, MDPI, vol. 10(13), pages 1-20, July.
    10. Samira Rastbod & Farnaz Rahimi & Yara Dehghan & Saeed Kamranfar & Omrane Benjeddou & Moncef L. Nehdi, 2022. "An Optimized Machine Learning Approach for Forecasting Thermal Energy Demand of Buildings," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    11. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    12. Smolenski, Robert & Szczesniak, Pawel & Drozdz, Wojciech & Kasperski, Lukasz, 2022. "Advanced metering infrastructure and energy storage for location and mitigation of power quality disturbances in the utility grid with high penetration of renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).

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