Swarm and evolutionary algorithms for energy disaggregation: challenges and prospects

Samira Ghorbanpour, T. Pamulapati, R. Mallipeddi
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引用次数: 6

Abstract

Energy disaggregation is defined as the process of estimating the individual electrical appliance energy consumption of a set of appliances in a house from the aggregated measurements taken at a single point or limited points. The energy disaggregation problem can be modelled both as pattern recognition problem and as an optimisation problem. Among the two, the pattern recognition problem has been considerably explored while the optimisation problem has not been explored to the potential. In literature, researchers have attempted to solve the problem using various optimisation algorithms including swarm and evolutionary algorithms. However, the focus on optimisation-based methodologies, in general, swarm and evolutionary algorithm-based methodologies in particular is minimal. By considering the different problem formulations in the literature, we propose a framework to solve the energy disaggregation problem with swarm and evolutionary algorithms. With the help of simulation results using the existing problem formulations, we discuss the challenges posed by the energy disaggregation to swarm and evolutionary algorithm-based methodologies and analyse the prospects of these algorithms for the problem of energy disaggregation with some future directions.
能量分解的群算法和进化算法:挑战与展望
能源分解的定义是,根据在单个点或有限点进行的汇总测量,估计房屋中一组电器的单个电器能耗的过程。能量分解问题既可以建模为模式识别问题,也可以建模为优化问题。其中,模式识别问题已经得到了相当大的探索,而优化问题尚未探索到潜在的。在文献中,研究人员已经尝试使用各种优化算法来解决问题,包括群算法和进化算法。然而,一般来说,对基于优化的方法,特别是基于群体和进化算法的方法的关注很少。在考虑文献中不同问题表述的基础上,提出了一种利用群算法和进化算法求解能量分解问题的框架。利用现有问题公式的仿真结果,讨论了能量分解对基于群算法和进化算法的能量分解方法的挑战,并分析了这些算法解决能量分解问题的前景和未来发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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