Abstract
Concerning electrical energy used in today’s modern society, electrical energy demands requested from downstream sectors in a smart grid are continuously increasing. One way to meet the electrical demands requested is to monitor and manage industrial, commercial, as well as residential electrical appliances efficiently in response to Demand Response (DR) programs for Demand-Side Management (DSM). Monitoring and managing electrical appliances that consume electrical energy in fields of interest can be realized through use of Energy Management Systems (EMS) with Non-Intrusive Load Monitoring (NILM). This paper presents an Internet of Things (IoT)-oriented Home EMS (HEMS). Also, a novel hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO)-integrated NILM approach is proposed and used to model and identify electrical appliances for DSM in the HEMS. ANN can be applied in NILM as a load identification task. Nevertheless, the performance of ANN used for load identification depends on three principal design factors: The network topology designed, the type of activation functions chosen, and the training algorithm adopted. As a result, PSO is conducted and used to incorporate meta-heuristics with ANN considering the three principal design factors relating to an ANN design. The HEMS with the novel hybrid ANN-PSO-integrated NILM proposed in this paper was deployed and evaluated in a realistic residential house environment. As the experimentation reported in this paper shows, the presented HEMS utilizing the proposed novel hybrid ANN-PSO-integrated NILM to model and identify monitored electrical appliances is feasible and workable, with an overall classification rate of 91.67% in load classification for DSM.
Highlights
Nowadays, owing to global warming and climate change, it is very critical to monitor and manage industrial, commercial, and residential individual electrical appliances such that (1) the efficiency of electrical energy used in modern society and requested from downstream sectors of a smart grid can be improved, and (2) the pollution of greenhouse gases such as carbon dioxide can be mitigated
To participate in Demand-Side Management (DSM), the first step is to keep track of electrical energy used by consumers and that consumed by individual electrical appliances in fields of interest
The novel hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-Particle Swarm Optimization (PSO))-integrated Non-Intrusive Load Monitoring (NILM) approach was used in the Home EMS (HEMS) as a benchmark, to non-intrusively identify uses of electrical appliances consuming electrical energy
Summary
Nowadays, owing to global warming and climate change, it is very critical to monitor and manage industrial, commercial, and residential individual electrical appliances such that (1) the efficiency of electrical energy used in modern society and requested from downstream sectors of a smart grid can be improved, and (2) the pollution of greenhouse gases such as carbon dioxide can be mitigated. Can be used to intrusively keep track of electric power consumption on monitored individual electrical household appliances, where plug-load smart e-meters are installed and used in a field of interest. The HEMS was constructed, conducted, and used as a benchmark to intrusively acquire electrical power consumption on monitored individual electrical appliances through ZigBee. The novel hybrid ANN-PSO-integrated NILM approach was used in the HEMS as a benchmark, to non-intrusively identify uses of electrical appliances consuming electrical energy. The experimentation reported in this paper showed that the HEMS utilizing the proposed methodology to design an ANN for load identification in NILM gave an overall classification rate of 91.67%.
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