The paper addresses the challenge of optimizing residential energy consumption in real-time pricing environments, where users can benefit from reduced electricity costs by adjusting their appliance usage based on time-varying prices. The authors propose an optimal and automatic residential energy consumption scheduling framework that minimizes both electricity payments and waiting times for appliance operation. They emphasize the importance of price prediction capabilities, especially when utility companies provide price information only a few hours in advance. The framework uses a weighted average price prediction filter to estimate future prices, which are then used in a linear programming optimization problem to determine the optimal energy consumption schedule. The proposed method is computationally efficient and can be implemented with minimal user effort. Simulation results show significant reductions in electricity payments and peak-to-average load demand ratios for various load scenarios, demonstrating the benefits for both users and utility companies.The paper addresses the challenge of optimizing residential energy consumption in real-time pricing environments, where users can benefit from reduced electricity costs by adjusting their appliance usage based on time-varying prices. The authors propose an optimal and automatic residential energy consumption scheduling framework that minimizes both electricity payments and waiting times for appliance operation. They emphasize the importance of price prediction capabilities, especially when utility companies provide price information only a few hours in advance. The framework uses a weighted average price prediction filter to estimate future prices, which are then used in a linear programming optimization problem to determine the optimal energy consumption schedule. The proposed method is computationally efficient and can be implemented with minimal user effort. Simulation results show significant reductions in electricity payments and peak-to-average load demand ratios for various load scenarios, demonstrating the benefits for both users and utility companies.