Energy Management in Residential Microgrid Based on Non-Intrusive Load Monitoring and Internet of Things

Autor: Rawda Ramadan, Qi Huang, Amr S. Zalhaf, Olusola Bamisile, Jian Li, Diaa-Eldin A. Mansour, Xiangning Lin, Doaa M. Yehia
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Smart Cities, Vol 7, Iss 4, Pp 1907-1935 (2024)
Druh dokumentu: article
ISSN: 2624-6511
07595735
DOI: 10.3390/smartcities7040075
Popis: Recently, various strategies for energy management have been proposed to improve energy efficiency in smart grids. One key aspect of this is the use of microgrids. To effectively manage energy in a residential microgrid, advanced computational tools are required to maintain the balance between supply and demand. The concept of load disaggregation through non-intrusive load monitoring (NILM) is emerging as a cost-effective solution to optimize energy utilization in these systems without the need for extensive sensor infrastructure. This paper presents an energy management system based on NILM and the Internet of Things (IoT) for a residential microgrid, including a photovoltaic (PV) plant and battery storage device. The goal is to develop an efficient load management system to increase the microgrid’s independence from the traditional electrical grid. The microgrid model is developed in the electromagnetic transient program PSCAD/EMTDC to analyze and optimize energy performance. Load disaggregation is obtained by combining artificial neural networks (ANNs) and particle swarm optimization (PSO) to identify appliances for demand-side management. An ANN is applied in NILM as a load identification task, and PSO is used to optimize the ANN algorithm. This combination enhances the NILM technique’s accuracy, which is verified using the mean absolute error method to assess the difference between the predicted and measured power consumption of appliances. The NILM output is then transferred to consumers through the ThingSpeak IoT platform, enabling them to monitor and control their appliances to save energy and costs.
Databáze: Directory of Open Access Journals