A novel optimization method combining metaheuristics and machine learning for daily optimal operations in building energy and storage systems
A novel optimization method combining metaheuristics and machine learning for daily optimal operations in building energy and storage systems
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DOI：
10.1016/j.apenergy.2021.116716
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发表时间：
2021-05
期刊：
影响因子：
11.2
通讯作者：
S. Ikeda;T. Nagai
中科院分区：
文献类型：
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作者：
S. Ikeda;T. Nagai
近年来，建筑物和区域能源系统的运行优化研究一直在积极进行。有几个小组利用线性近似，考虑非线性，进行基于神经网络的研究，并使用优化算法来找到最佳解决方案。在建筑物中的实际实施方面，应在实际计算时间内考虑机器特性的非线性，因为线性化会产生建模成本，并且计算资源有限。因此，作者提出了一种混合算法，包括元算法和机器学习，用于优化建筑能源系统的日常运行时间表。深度神经网络机器学习技术用于预测集成冷却塔系统的最佳运行，元分析用于优化其他组件的运行。所提出的方法可以减少超过13.4%的日常运营成本。此外，在这项研究中评估的集成冷却塔系统降低了成本和能源需求相比，单独的冷却塔系统。
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.