基于粒子群算法的工业与商业建筑能耗分解
Particle Swarm Optimization for Energy Disaggregation in Industrial and Commercial Buildings
摘要：
本文提供了一种形式化的粒子群优化的能量分解问题，并显示了粒子群优化分解在多租户商业建筑的成功应用。使用状态变化矩阵的分解问题的数学描述属于用于能量分解的基于非事件的方法的组。这项工作包括在功率域的目标函数的发展和描述的位置和速度的每个粒子在高维状态空间。对于粒子群优化算法，四个自适应已被应用到改善解集的结果，增加了鲁棒性的优化器关于局部最优，并减少计算时间。自适应是变化的运动常数，粒子的振动，框架和早期停止标准。在这项工作中，我们使用两个未标记的功率数据集，粒度为1 s。因此，在功率域中验证了结果，其中可以显示关于多个误差测量（如均方根误差或百分比能量误差）的良好结果。
英文摘要：
This paper provides a formalization of the energy disaggregation problem for particle swarm optimization and shows the successful application of particle swarm optimization for disaggregation in a multi-tenant commercial building. The developed mathmatical description of the disaggregation problem using a state changes matrix belongs to the group of non-event based methods for energy disaggregation. This work includes the development of an objective function in the power domain and the description of position and velocity of each particle in a high dimensional state space. For the particle swarm optimization, four adaptions have been applied to improve the results of disaggregation, increase the robustness of the optimizer regarding local optima and reduce the computational time. The adaptions are varying movement constants, shaking of particles, framing and an early stopping criterion. In this work we use two unlabelled power datasets with a granularity of 1 s. Therefore, the results are validated in the power domain in which good results regarding multiple error measures like root mean squared error or the percentage energy error can be shown.
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