Towards simulating the constraint-based nature-inspired smart scheduling in energy intelligent buildings

Abstract

Appliance scheduling on the user’s defined preferences plays a pivotal role in smart home energy management systems. In this context, energy management controllers are largely used to satisfy the users’ demand preferences within their financial budget constraints. This work proposes a robust technique based on demand-side energy management for efficiently monitoring and controlling the domestic loads. A nature-inspired crescive consumer satisfaction algorithm (NCSA) is proposed for the optimal scheduling pattern based on time and device preferences. The proposed algorithm maximizes user satisfaction at the preset user budget by producing optimum appliance scheduling patterns. It considers household appliances input data such as time of use, power ratings, and the absolute maximum satisfaction for optimal scheduling. The proposed algorithm is evaluated on three budget schemes, and the simulation results reveal that the proposed algorithm achieves a better satisfaction index at a lower cost per unit satisfaction. Results also show that the proposed algorithm has a good convergence rate and is generalizable to any random budget scenario.