Artificial Intelligence-based scheduling with Multi-Objective Power Optimization in Smart Homes Utilizing Particle Swarm Optimizer

This research proposes a novel approach that integrates distinct Artificial Intelligence (AI) methodologies to address the issue of energy demand planning in Smart Homes (SH). The novel technique is designed to address a multi-objective scheduling issue, aiming to strike a balance between energy consumption and user convenience. The AI-based Power Scheduling issue in a Smart Home (AI-PSSH) involves efficiently scheduling intelligent home equipment within specified constraints and flexible pricing schemes. Various methods have been suggested to tackle the problem of AI-PSSH effectively, including both optimal and non-optimal strategies. This study presents a novel formulation for the Intelligent Home Batteries (IHB) in the context of AI-PSSH, which mitigates the impact of constraints in achieving the optimum or near-optimum outcomes. IHB may optimize the scheduling of intelligent home devices by saving excess power when it is not needed and using saved power when it is more suited to achieve the goals of PSSH. PSSH is a multi-objective optimization problem aimed at concurrently achieving all goals. Incorporating AI into smart homes has facilitated the development of sophisticated energy management techniques in scheduling and power optimization. The approach utilizes Particle Swarm Optimization (PSO) to improve the effectiveness of scheduling algorithms. Integrating PSO into the scheduling process enhances flexibility and enables smart decision-making based on real-time adjustments. The simulation results assess the proposed multi-objective AI-PSSH utilizing the IHB (MO-AI-PSSH) strategy by considering five possibilities of energy use and flexible pricing strategies. The efficiency of the suggested MO-AI-PSSH technique is compared to that of existing cutting-edge algorithms using the suggested databases utilizing the provided datasets. The MO-AI-PSSH technique, as suggested, demonstrates superior performance compared to the other algorithms in almost all instances.