Building energy management usually involves a number of objectives, such as investment costs, thermal comfort, system resilience, battery life, and many others. However, most existing studies merely consider optimizing less than three objectives since it becomes increasingly difficult as the number of objectives increases. In addition, the optimization of building energy management relies heavily on time-consuming energy component simulators, posing great challenges for conventional evolutionary algorithms that typically require a large number of real function evaluations. To address the above-mentioned issues, this paper formulates a building energy management scenario as a 10-objective optimization problem, aiming to find optimal configurations of power supply components. To solve this expensive many-objective optimization problem, six state-of-the-art multi-objective evolutionary algorithms, five of which are assisted by surrogate models, are compared. The experimental results show that the adaptive reference vector assisted algorithm is proven to be the most competitive one among the six compared algorithms; the five evolutionary algorithms with surrogate assistance always outperform their counterpart without the surrogate, although the kriging-assisted reference vector assisted evolutionary algorithm only performs slightly better than the algorithm without surrogate assistance in dealing with the 10-objective building energy management problem. By analyzing the non-dominated solutions obtained by the six algorithms, an optimal configuration of power supply components can be obtained within an affordable period of time, providing decision makers with new insights into the building energy management problem.

中文翻译：

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建筑能源管理的替代辅助多目标优化


建筑能源管理通常涉及许多目标，例如投资成本、热舒适度、系统弹性、电池寿命等。然而，大多数现有研究仅考虑优化少于三个目标，因为随着目标数量的增加，它变得越来越困难。此外，建筑能源管理的优化严重依赖于耗时的能源成分模拟器，这对通常需要大量真实功能评估的传统进化算法提出了巨大的挑战。针对上述问题，本文将建筑能源管理场景制定为10目标优化问题，旨在寻找供电组件的最优配置。为了解决这个昂贵的多目标优化问题，比较了六种最先进的多目标进化算法，其中五种由代理模型辅助。实验结果表明，自适应参考向量辅助算法被证明是六种对比算法中最具竞争力的算法；尽管在处理 10 目标建筑能源管理问题时，克里格辅助参考向量辅助进化算法仅比没有替代辅助的算法稍好一些，但有替代辅助的五种进化算法总是优于没有替代辅助的算法。通过分析六种算法得到的非支配解，可以在可承受的时间内获得电源组件的最优配置，为决策者提供对建筑能源管理问题的新见解。