Artificial Intelligence, Accelerated in Parallel Computing and Applied to Nonintrusive Appliance Load Monitoring for Residential Demand-Side Management in a Smart Grid: A Comparative Study

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.

Impact of the Lockdown during the COVID-19 Pandemic on Electricity Use by Residential Users

“…It necessitates the proposition of models that minimise the electricity cost and user discomfort while taking into account the peak energy consumption. For residential DSRs, approaches based on Artificial Intelligence (AI) are used across the Internet of Things (IoT), e.g., [60,61].…”

Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

“…In this research, an experiment was carried out to experimentally validate the effectiveness of the proposed energy decomposition approach, a k-Means-clustering-hybridized neuro-fuzzy classifier metaheuristically optimized by a parallel GA, that decomposes composite/circuit-level electric energy consumption into appliance-level electric energy consumption (which is addressed as an appliance classification problem) via energy decomposition. The UK-DALE dataset [27], conditioned in [7], was considered and used as a smart meter dataset. Figure 5 shows the considered historical power demand on Sunday 7 December 2014 in House 1, which was monitored and recorded in the UK-DALE dataset [27], conditioned in [7] and addressed in this research.…”

“…The basic event-less energy decomposition process can be referenced in [7], where load recognition and load classification that utilizes AI, k-Means-clustering-hybridized and parallel-computing-accelerated GA-evolved neuro-fuzzy classification to recognize extracted electrical features for relevant electrical appliances was investigated.…”

“…Moreover, monitoring relevant electrical appliances in an intrusive style is impractical as the investment costs, including costs for the installation and maintenance of infrastructure, are high [5]. Therefore, energy decomposition, so-called non-intrusive load monitoring [5][6][7][8][9][10], has been developed. Energy decomposition, as part of an EMS, is a process of decomposing composite/circuit-level electric energy consumption into appliance-level electric energy consumption through examining appliance-specific characteristics such as power consumption [6]; this comes with the advantage of requiring no additional plug-level power meters to be deployed for relevant individual electrical appliances while maintaining the investment costs for the energy management system, including its installation and annual maintenance costs, at a minimum.…”

“…Then, the time duration for which each of the electrical appliances was used can be further figured out, which is useful for actionable insights wherein the use of electrical appliances is scheduled in response to electricity pricing for DSM. Energy decomposition can be built upon an Artificial Intelligence (AI) methodology including metaheuristics [7][8][9][10][11][12][13][14][15][16], which ranges from machine learning to deep learning. For instance, in [9], the random forest, a meta estimator that fits a number of decision tree classifiers on various subsamples of training data and uses averaging/majority voting to improve predictive performance and control over-fitting, was used as the learning algorithm to determine which categories feature data belong to for energy decomposition.…”