Joint Peak Clipping and Load Scheduling Based on User Behavior Monitoring in an IoT Platform

Abstract

This article proposes a demand-side management (DSM) mechanism for energy management based on user behavior monitoring in a smart home. In the proposed mechanism, first through an analytic hierarchy process, the most influential factors related to power consumption are extracted. Next, by employing the K-means algorithm on the extracted factors, users are clustered. The user's clusters, the power grid state, and the user's real-time power consumption are inputs for a control unit. We present an interactive algorithm for the control unit, which causes peak reduction using peak clipping techniques. We also develop a day-ahead scheduling mechanism, which optimizes the load based on load shifting techniques. The proposed system is implemented in an Internet of Things (IoT) testbed consisting of four tiers-sensors, home gateways, server, and web portal. The central server is based on the Kaa IoT platform, an open-source platform widely used in the IoT domain. The performance of the proposed system is evaluated through simulation and a case study. Results confirm that the proposed system reduces the power consumption and costs for users and improves power grid performance in terms of the peak-to-average ratio.