Optimization-Based Energy Disaggregation: A Constrained Multi-Objective Approach

Abstract

Recently, optimization-based energy disaggregation (ED) algorithms have been gaining significance due to their capability to perform disaggregation with minimal information compared to the pattern-based ED algorithms, which demand large amounts of data for training. However, the performances of optimization-based ED algorithms depend on the problem formulation that includes an objective function(s) and/or constraints. In the literature, ED has been formulated as a constrained single-objective problem or an unconstrained multi-objective problem considering disaggregation error, sparsity of state switching, on/off switching, etc. In this work, the ED problem is formulated as a constrained multi-objective problem (CMOP), where the constraints related to the operational characteristics of the devices are included. In addition, the formulated CMOP is solved using a constrained multi-objective evolutionary algorithm (CMOEA). The performance of the proposed formulation is compared with those of three high-performing ED formulations in the literature based on the appliance-level and overall indicators. The results show that the proposed formulation improves both appliance-level and overall ED results.

A Multi-Task Decomposition-Based Evolutionary Algorithm for Tackling High-Dimensional Bi-Objective Feature Selection

“…Thus, a huge variety of MOEAs have been proposed all over the world and can be roughly divided into the following categories: dominance-based MOEAs [5][6][7][8], decompositionbased MOEAs [9][10][11][12][13], indicator-based MOEAs [14][15][16][17], surrogate-based MOEAs [18][19][20], cooperative coevolutionary MOEAs [21][22][23], multi-task MOEAs [24][25][26], and so on. There are also many other kinds of excellent MOEAs [27][28][29], including the novel multi-objective particle swarm optimization algorithm proposed by Leung et al [30], which adopted a hybrid global leader selection strategy with two leaders: one for exploration and the other for exploitation. Moreover, MOEAs have also been used to solve many real-world optimization problems [31][32][33], such as system control [34,35], community detection [36,37], network construction [38][39][40], task allocation [41,42], and feature selection [43,44].…”

A Dynamic Tasking-Based Evolutionary Algorithm for Bi-Objective Feature Selection

“…There are numerous other kinds of excellent MOEAs [57][58][59][60] that have been proposed around the world, many of which are used for real-world applications, such as intrusion detection in networks [61], efficient sensing in wireless sensor networks [62], control of building systems [63], menu planning in schools [64] and control of hybrid electric vehicle charging systems [65].…”

Consideration of Multi-Objective Stochastic Optimization in Inter-Annual Optimization Scheduling of Cascade Hydropower Stations

“…This implies that objective weights can be adjusted according to specific needs and conditions to meet different optimization requirements. Research results from references [17][18][19] indicate that these methods enhance problem diversity by generating multiple potential solutions, facilitating the discovery of diversified optimization strategies and providing more choices for decision making. Reference [20] notes that multi-objective optimization methods can provide decision makers with a series of optimized solutions, enabling them to better understand the trade-offs and consequences between different decision options, thus supporting the decision-making process and making it more transparent.…”