An Improved Optimization Function to Integrate the User’s Comfort Perception into a Smart Home Controller Based on Particle Swarm Optimization and Fuzzy Logic

Abstract

Scheduling residential loads for financial savings and user comfort may be performed by smart home controllers (SHCs). For this purpose, the electricity utility’s tariff variation costs, the lowest tariff cost schedules, the user’s preferences, and the level of comfort that each load may add to the household user are examined. However, the user’s comfort modeling, found in the literature, does not take into account the user’s comfort perceptions, and only uses the user-defined preferences for load on-time when it is registered in the SHC. The user’s comfort perceptions are dynamic and fluctuating, while the comfort preferences are fixed. Therefore, this paper proposes the modeling of a comfort function that takes into account the user’s perceptions using fuzzy logic. The proposed function is integrated into an SHC that uses PSO for scheduling residential loads, and aims at economy and user comfort as multiple objectives. The analysis and validation of the proposed function includes different scenarios related to economy–comfort, load shifting, consideration of energy tariffs, user preferences, and user perceptions. The results show that it is more beneficial to use the proposed comfort function method only when the user requires SHC to prioritize comfort at the expense of financial savings. Otherwise, it is more beneficial to use a comfort function that only considers the user’s comfort preferences and not their perceptions.