article · Smart Cities
Managing energy effectively in residential microgrids requires balancing electricity supply and demand while minimising reliance on the conventional power grid. An energy management system combining non-intrusive load monitoring with Internet of Things technology offers a cost-effective way to track household energy without widespread sensor installation. Within a simulated residential microgrid featuring solar photovoltaic generation and battery storage, an artificial neural network identifies specific household appliances, with particle swarm optimisation used to enhance identification accuracy. Evaluated via mean absolute error against measured appliance consumption, the disaggregated load data feeds into the ThingSpeak Internet of Things platform. This setup provides consumers with direct visibility and control over appliance usage, helping to regulate local demand and reduce energy costs while fostering microgrid autonomy.
Many households face rising energy costs, and power grids struggle with fluctuating renewable energy supplies. By identifying which appliances consume the most electricity without installing expensive sensors on every plug, this approach helps residents lower utility bills, balance local solar and battery usage, and relieve strain on national power networks.
This approach could enable smart home energy management solutions for residential consumers, microgrid operators, and energy service companies seeking to optimise demand without deploying costly sub-metering hardware. Currently tested within a PSCAD/EMTDC simulation environment linked to an Internet of Things cloud platform, the technology sits at an applied research stage and requires real-world physical deployment and field validation before commercial adoption.
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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.
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DOI: 10.3390/smartcities7040075
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