Abstract: China is in immediate need of implementing a robust market-driven time-of-use electricity pricing mechanism for residential consumers. This measure is crucial for guiding users in reducing electricity consumption during peak periods and increasing it during off-peak periods. Furthermore, it will facilitate the integration of renewable energy sources and contribute to the secure and stable economic functioning of the power system. This study employs smart meter electricity consumption data obtained from residential customers and integrates it with comprehensive survey questionnaire data on a broad scale. This approach integrates machine learning techniques with economic principles of supply and demand to develop power demand curves for diverse residential consumers. This study presents a quantitative approach that considers the diversity of individual households and the temporal variations in energy consumption patterns. It investigates the most effective time-of-use electricity pricing strategy for various objectives, such as reducing peak demand and filling off-peak periods. The findings from the simulation demonstrate a considerable opportunity for demand-side response among residential users. By employing a scientific approach to categorize peak and valley periods and establishing appropriate price differentials, it is possible to effectively address the imbalance between electricity supply and demand. In periods of exceptionally high temperatures throughout the summer season, implementing a greater price disparity between peak and off-peak hours can serve as an effective measure to mitigate the strain on the electrical grid during peak demand periods. Furthermore, the findings of this study indicate that when designing time-of-use pricing mechanisms, it is crucial to consider the diverse behavioral patterns exhibited by residential households and the variations across different time periods, as well as the unique characteristics of their energy supply systems.