Non–intrusive load disaggregation of smart home appliances using the IPPO algorithm and FHM model. (April 2021)

• Record Type: Journal Article
• Authors: Xia, Dong; Ba, Shusong; Ahmadpour, Ali
• Is Part Of: Sustainable cities and society. Volume 67(2021)
• Publication Date: 2021-04
• Subjects: AET Average Execution Time -- CPSOA Constrained Particle Swarm Optimization Algorithm -- DFS Dynamic Fixed Share -- EV Electrical Vehicle -- FA Feedback Algorithm -- FFANN Feed–Forward Artificial Neural Network -- FHMM Factorial Hidden Markov Model -- GSP Graph Signal Processing -- HMM Hidden Markov Model -- ILM Intrusive Load Monitoring -- IPPOA Improved Prey–Predator Optimization Algorithm -- LIPA Linear Integer Programming Algorithm -- LOA Lion Optimization Algorithm -- NILM Non–Intrusive Load Monitoring -- PPOA Prey–Predator Optimization Algorithm -- RFPSO Random Forest optimized by Particle Swarm Optimization -- SIQCP Segmented Integer Quadratic Constraint Programming -- STD State Transition Diagram -- TS Tabu Search; Smart homes -- Domestic appliances -- Energy prediction -- Non–intrusive load disaggregation -- Improved Prey–Predator Optimization Algorithm -- Factorial Hidden Markov Model; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2021.102731
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: The allowable states matrix of all appliances extracts with the FHMM based on STD. The estimated responses limits with three constraints to more concentration. A novel hybrid method with IPPOA is used to enhance the results accuracy. Results show the proposed method is better from accuracy and response speed sights. Abstract: At present, producing the lowest power requires prognosticating the load operations in the home automation systems. Several electrical household devices with various performances work in smart homes. These variances are defined as the positions of the electrical devices. In the current study, a practical mixed method is suggested for load disaggregation of the electrical devices. A highly precise model of the Factorial Hidden Markov Model (FHMM) is applied for electrical device positions simulating. In the proposed method, the present position of every electrical device is obtained and available in the dataset. Then, the distinct suitable positions for the following instant are supplied. To obtain the best approximation of positions, it is reported to the Improved Prey–Predator Optimization Algorithm (IPPOA). Additionally, three restraints are applied in IPPOA for better adjusting the position matrix. The first one is that every electrical device must possess one position at any stage. Second, regarding the continuous active electrical household devices is necessary. Third, the FHMM should be implemented for load model generation. In terms of the third restraint, the assessed databanks and the computation period are notably decreased owing to the utilization of the FHMM. The quickness and precision of the answers for useful data of six smart homes are evaluated against other techniques to confirm the efficiency of the proposed technique.