Intelligent Thermostat Learning for Low-Complexity Energy Saving

The intelligent thermostat automatically learns and adapts to user temperature preferences and occupancy patterns without requiring manual programming. The system performs self-configuration by monitoring user behavior and environmental data, eliminating the need for users to navigate complex settings while achieving energy-saving goals through automated optimization of HVAC operation schedules.

The system dynamically adjusts temperature setpoints and HVAC operation parameters based on learned user preferences and real-time environmental conditions. By continuously optimizing control parameters through automated learning algorithms, the thermostat achieves energy savings without requiring users to understand or configure multiple settings.

The intelligent thermostat implements periodic operation cycles with active learning and wireless communication phases alternating with low-power sleep phases. The system performs high-power activities such as wireless data transmission and environmental sensing at scheduled intervals rather than continuously, significantly reducing average power consumption while maintaining adaptability through periodic updates and learning cycles.

The patent replaces traditional mechanical thermostat components with electronic and software-based systems that consume less power. The use of microprocessors and software algorithms for learning and control replaces older mechanical sensing and timing mechanisms, enabling intelligent functionality with lower overall power requirements when combined with periodic operation.