Optimizing grid-connected solar PV-powered smart homes: IoT-based energy management systems using AOA-PHNN approach

Home energy management is becoming more and more popular due to the Internet of Things (IoT). This is referred to as “intelligent electricity use,” and it is made possible by the development of smart grid (SG) and electrical technologies. They combine the energy generation of solar panels with the automation and convenience of smart home technology. This creates a home environment that is not only comfortable and efficient but also minimizes its environmental impact. 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. The proposed method's primary goal is to minimize the energy conversion losses (ECL).The PHNN method predicts the building load demand. The AOA technique is used to optimize the scheduling of energy consumptions for smart home appliances. By then the performance of the proposed hybrid technique is implemented in the MATLAB platform and contrasted with various existing techniques. The proposed technique shows low-cost value is 1 \(\times\) $\times$ 10³ Rs and low-energy conversion loss value is 1(%) compared with other existing methods such as several optimization algorithms (SOA), backtracking search optimization algorithm (BSA), and grasshopper optimization algorithm (GOA), respectively.