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.

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