This paper proposes a hybrid technique for optimizing grid-connected solar PV-powered smart homes: IoT-based energy management systems. The proposed technique is the joint operation of both the arithmetic optimization algorithm (AOA) and pseudo-Hamiltonian neural networks (PHNNs). Hence, it is named as AOA-PHNN technique.
However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN).
This study proposes optimizing grid-connected solar PV-powered smart homes: IoT-based energy management systems. Since one algorithm cannot always handle an issue to a satisfactory degree, two methods, like AOA-PHNN, can be used.
A Smart Grid ecosystem refers to the interconnected network of various components involved in the generation, distribution, and consumption of electricity, which utilise advanced communication, automation, and IT systems to improve the efficiency, reliability, and sustainability of electricity services (Dileep 2020).
Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas.
As concerns about fossil fuel scarcity and environmental impact grow, the integration, of renewable energy sources (RESs), such as solar power, into microgrids (MGs) has become increasingly important . These systems can enhance local reliability, reduce power losses, and provide necessary electrical and thermal energy.
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