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Genetic-programming-based multi-objective optimization of strategies for home energy-management systems

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  • Zupančič, Jernej
  • Filipič, Bogdan
  • Gams, Matjaž

Abstract

Home energy-management systems can optimize performance either by computing the next step dynamically – online, or rely on a precomputed strategy used to introduce the next decision – offline. Further, such systems can optimize based on only one or several objectives. In this paper, the multi-objective optimization of offline strategies for home energy-management systems is addressed. Two approaches are compared: the common timetable-based versus our approach based on decision trees. The timetable-based strategy is optimized using a multi-objective genetic algorithm, while the tree-based strategy is optimized using multi-objective genetic programming. As a result, a set of rules that comprise the trees for efficient management of an energy system is generated automatically. First, the approaches are addressed theoretically, with the finding that the tree-based approach is more powerful than the timetable-based approach. Second, the performance of the tree-based approach is compared with the performance of the timetable-based approach and manually defined strategies in an experiment involving real-world data. A performance increase of up to 17% in terms of the cost objective was confirmed for the tree-based approach. This is achieved without changing the user habits, i.e., there is no need of having to adapt the appliance usage to the energy-management system.

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  • Zupančič, Jernej & Filipič, Bogdan & Gams, Matjaž, 2020. "Genetic-programming-based multi-objective optimization of strategies for home energy-management systems," Energy, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:energy:v:203:y:2020:i:c:s0360544220308768
    DOI: 10.1016/j.energy.2020.117769
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    Cited by:

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    2. Dewangan, Chaman Lal & Vijayan, Vineeth & Shukla, Devesh & Chakrabarti, S. & Singh, S.N. & Sharma, Ankush & Hossain, Md. Alamgir, 2023. "An improved decentralized scheme for incentive-based demand response from residential customers," Energy, Elsevier, vol. 284(C).
    3. Youssef, Heba & Kamel, Salah & Hassan, Mohamed H. & Nasrat, Loai, 2023. "Optimizing energy consumption patterns of smart home using a developed elite evolutionary strategy artificial ecosystem optimization algorithm," Energy, Elsevier, vol. 278(C).
    4. Wang, Huaqing & Xie, Zhuoshi & Pu, Lei & Ren, Zhongrui & Zhang, Yaoyu & Tan, Zhongfu, 2022. "Energy management strategy of hybrid energy storage based on Pareto optimality," Applied Energy, Elsevier, vol. 327(C).
    5. Zehra, Syeda Shafia & Ur Rahman, Aqeel & Ahmad, Iftikhar, 2022. "Fuzzy-barrier sliding mode control of electric-hydrogen hybrid energy storage system in DC microgrid: Modelling, management and experimental investigation," Energy, Elsevier, vol. 239(PD).
    6. Binghui Han & Younes Zahraoui & Marizan Mubin & Saad Mekhilef & Mehdi Seyedmahmoudian & Alex Stojcevski, 2023. "Optimal Strategy for Comfort-Based Home Energy Management System Considering Impact of Battery Degradation Cost Model," Mathematics, MDPI, vol. 11(6), pages 1-26, March.
    7. Lu, Zhiming & Gao, Yan & Xu, Chuanbo, 2021. "Evaluation of energy management system for regional integrated energy system under interval type-2 hesitant fuzzy environment," Energy, Elsevier, vol. 222(C).

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