Real-time electricity pricing (RTP) and inclining block rates (IBR) offer economic and environmental benefits over flat-rate pricing by allowing users to respond to time-varying prices. However, users often lack knowledge on how to respond, and effective building automation systems are needed to fully utilize these benefits. This paper proposes an optimal residential energy consumption scheduling framework that minimizes electricity payments and waiting times for appliance operations. The framework uses simple linear programming and incorporates price prediction to account for future pricing. A weighted average price prediction filter is applied to historical price data from the Illinois Power Company to determine optimal coefficients for each day of the week. Simulation results show that combining this scheduling design with the price predictor filter significantly reduces user payments and peak-to-average load ratios. The framework requires minimal user effort and is based on real-time pricing and IBR. The proposed method is computationally feasible and can be implemented in smart meters with two-way communication. The paper also discusses various extensions, including handling discrete energy consumption levels, uninterruptible loads, multiple utility sources, avoiding load synchronization, and accommodating changes in user energy needs. The results demonstrate that the proposed optimal energy consumption scheduling schemes benefit both end users and utility companies.Real-time electricity pricing (RTP) and inclining block rates (IBR) offer economic and environmental benefits over flat-rate pricing by allowing users to respond to time-varying prices. However, users often lack knowledge on how to respond, and effective building automation systems are needed to fully utilize these benefits. This paper proposes an optimal residential energy consumption scheduling framework that minimizes electricity payments and waiting times for appliance operations. The framework uses simple linear programming and incorporates price prediction to account for future pricing. A weighted average price prediction filter is applied to historical price data from the Illinois Power Company to determine optimal coefficients for each day of the week. Simulation results show that combining this scheduling design with the price predictor filter significantly reduces user payments and peak-to-average load ratios. The framework requires minimal user effort and is based on real-time pricing and IBR. The proposed method is computationally feasible and can be implemented in smart meters with two-way communication. The paper also discusses various extensions, including handling discrete energy consumption levels, uninterruptible loads, multiple utility sources, avoiding load synchronization, and accommodating changes in user energy needs. The results demonstrate that the proposed optimal energy consumption scheduling schemes benefit both end users and utility companies.