Abstract
The key advantage of smart meters over rotating-disc meters is their ability to transmit electric energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electric energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence (AI) make the realization of consumer-centric use cases possible. A smart meter installed in a domestic sector of an electrical grid and used for the realization of consumer-centric use cases is located at the entry point of a household/building’s electrical grid connection and can gather composite/circuit-level electric energy consumption data. However, it is not able to decompose its measured circuit-level electric energy consumption into appliance-level electric energy consumption. In this research, we present an AI model, a neuro-fuzzy classifier integrated with partitional clustering and metaheuristically optimized through parallel-computing-accelerated evolutionary computing, that performs energy decomposition on smart meter data in residential demand-side management, where a publicly available UK-DALE (UK Domestic Appliance-Level Electricity) dataset is used to experimentally test the presented model to classify the On/Off status of monitored electrical appliances. As shown in this research, the presented AI model is effective at providing energy decomposition for domestic consumers. Further, energy decomposition can be provided for industrial as well as commercial consumers.
Highlights
Nowadays, the electricity energy demand requested from downstream sectors of an electrical grid is continuously increasing
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 Genetic Algorithm (GA), that decomposes composite/circuit-level electric energy consumption into appliance-level electric energy consumption via energy decomposition
Smart meter data, comprising composite/circuit-level electric energy consumption data captured at the entry point of a household/building’s electrical grid connection, can be further analyzed and used for the realization of useful consumer-centric use cases
Summary
The electricity energy demand requested from downstream sectors of an electrical grid is continuously increasing. DSM is the most promising technology that encourages consumers to optimize their electric energy consumption (to adjust their energy consumption behavior and usage habits [4]) Such that: (1) the ever-increasing energy demand requested from downstream sectors of an electrical grid can be met; (2) the efficiency, reliability, and flexibility of the electrical grid can be improved; and (3) emissions of greenhouse gas such as carbon dioxide and chlorofluorocarbons can be abated. 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. 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
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