Optimal appliance scheduling in a residential building using an improved binary algorithm with an inverse hyperbolic tangent function

Energy management in smart homes must evolve to efficiently balance electricity demand and minimize costs under dynamic tariff structures of demand response programs. Unlike existing studies that focus on non-interruptible appliances, the proposed approach highlights interruptible devices, such as electric water heaters and electric vehicles with both fast and slow charging options. The proposed study modified the scheduling problem into a binary optimization problem. In this paper, an Inverse Hyperbolic Tangent Binary Zebra Optimization Algorithm (IHTBZOA) is proposed that can efficiently manage the required energy of a smart home based on binary optimization challenges. It applies the methodology to the case study comprising 12 smart home appliances, including fast and slow EV chargers. In this case, there are 308 decision variables. A new set of constraints is also proposed to realistically represent EV charging modes and ensure session continuity without overlapping. The outcome of the optimization framework is explored using two tariffs: Real-Time Pricing (RTP) and Time-of-Use (TOU). Simulation results show that IHTBZOA achieves a reduction of up to 73–78 % in electricity costs and a significant improvement in convergence speed compared to other binary algorithms. The statistical analysis confirms IHTBZOA’s superiority with the lowest mean cost (0.867 for RTP and 1.9174 for TOU) and highest consistency (Feasibility Rank = 1). Moreover, when user discomfort is considered, IHTBZOA maintains optimal comfort levels with marginal increases in total cost. Conclusively, the obtained results demonstrated that IHTBZOA can be utilized to enhance the home energy management system by optimizing consumption patterns, without compromising user satisfaction.