Game-Theoretic Decision Making for Intelligent Power Consumption Analysis

With smart electricity distribution and dependable electric appliances, the revolutionary impact of the Internet of Things (IoT) technology has considerably improved the service-oriented features of the power grid industry. In the current study, a methodology for IoT-based electricity distribution for intelligent homes is described to detect power consumption efficiently. Although effective power resource allocation remains a primary issue for every power grid house, poor energy distribution has significantly influenced everyday living. The current study focuses on the effective distribution of electricity resources by power grid houses over a spatial-temporal basis. Specifically, the spatial-temporal consumption index is calculated for each home in a geographical region based on electricity usage, which enables the effective allocation of power resources. Additionally, an automated game-theoretic decision-making model is proposed to assist power grid house managers in optimizing the spatial-temporal distribution of electricity resources. For validation purposes, a simulated environment is used to monitor four smart houses for 60 days. A comparative analysis with state-of-the-art data assessment methodologies shows that the presented approach is significantly better in terms of statistical parameters of temporal delay (113.24 s), classification efficacy [precision (93.23%), sensitivity (92.34%), and specificity (92.34%)], decision-making efficiency, reliability (88.45%), and stability (72%).