Abstract
A smart grid is a promising use-case of AIoT (AI (artificial intelligence) across IoT (internet of things)) that enables bidirectional communication among utilities that arises with demand response (DR) schemes for demand-side management (DSM) and consumers that manage their power demands according to received DR signals. Disaggregating composite electric energy consumption data from a single minimal set of plug-panel current and voltage sensors installed at the electric panel in a practical field of interest, nonintrusive appliance load monitoring (NIALM), a cost-effective load disaggregation approach for (residential) DSM, is able to discern individual electrical appliances concerned without accessing each of them by individual plug-load power meters (smart plugs) deployed intrusively. The most common load disaggregation approaches are based on machine learning algorithms such as artificial neural networks, while approaches based on evolutionary computing, metaheuristic algorithms considered as global optimization and search techniques, have recently caught the attention of researchers. This paper presents a genetic algorithm, developed in consideration of parallel evolutionary computing, and aims to address NIALM, whereby load disaggregation from composite electric energy consumption data is declared as a combinatorial optimization problem and is solved by the algorithm. The algorithm is accelerated in parallel, as it would involve large amounts of NIALM data disaggregated through evolutionary computing, chromosomes, and/or evolutionary cycles to dominate its performance in load disaggregation and excessively cost its execution time. Moreover, the evolutionary computing implementation based on parallel computing, a feed-forward, multilayer artificial neural network that can learn from training data across all available workers of a parallel pool on a machine (in parallel computing) addresses the same NIALM/load disaggregation. Where, a comparative study is made in this paper. The presented methodology is experimentally validated by and applied on a publicly available reference dataset.
Highlights
Nonintrusive appliance load monitoring (NIALM), called nonintrusive load monitoring, was first investigated by George W
A smart grid is a promising use-case of AIoT (AI across IoT) that enables bidirectional communication among utilities that come up with demand response (DR) schemes for demand-side management (DSM) and consumers who manage their power demands according to received DR signals
NIALM, a cost-effective load disaggregation approach for DSM, is able to disaggregate measured total power consumption into appliance-level power consumption based on unique electrical characteristics extracted from electrical appliances concerned
Summary
Nonintrusive appliance load monitoring (NIALM), called nonintrusive load monitoring, was first investigated by George W. It has been considered as a cost-effective alternative against intrusive load monitoring approaches that involve the deployment. Sci. 2020, 10, 8114 of plug-load power meters (smart plugs) for individual concerned electrical appliances in a practical field of interest (it cannot be costed down for the realization of load management) [2], and has been developed for (residential) demand-side management (DSM) in a smart grid [3,4].
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