Senhome: a convenient and inexpensive sensing system for improving the energy efficiency of heating, cooling, and lighting in homes

Energy is among the most important issues in the world today. Buildings are responsible for almost half of all energy consumption annually in the world, and are therefore essential to any energy management strategy worldwide. Most efforts to increase building efficiency focus on weatherization and equipment efficiency. However, retrofitting is an expensive endeavor. The progress toward long-term energy goals is largely limited by short-term availability of capital investment. Thus, new technologies must be developed that can reduce the energy consumption of a building without requiring a large initial monetary investment. Wireless sensor networks (WSNs) enable ubiquitous sensing and smarter control in buildings to save energy with a much smaller initial monetary cost than existing solutions that require physical building and equipment upgrades. In this dissertation, we provide a cost-effective sensing system called SenHome that saves energy in residential buildings by sensing the information about home occupancy and physical environment, predicting future conditions by analyzing patterns in historical sensor data, and automatically configuring and optimizing building operation for energy efficiency. Our system uses motion and light sensors to reduce two major energy end-uses in homes: space conditioning (heating and cooling) and lighting, and the system automatically configures itself to obviate the cost of professional installation. It includes three major components. First, the Smart Thermostat uses occupancy statistics in a home in order to save energy through improved control of the HVAC system. Second, SunCast is a novel sunlight prediction framework that uses historical data traces to produce a continuous distribution of predicted sunlight values. Finally, the Place-N-Play system uses a combination of motion sensors and light sensors and facilitates sensor configuration by automatically inferring the floor plan of a home and the locations of these sensors. This dissertation lays the foundation for next-generation smart homes that will autonomously sense the building environment and strategically control building operations to achieve improved energy efficiency. The principles and approaches developed in this dissertation can be applied to commercial buildings and many other aspects of building operation. Our technology has the potential for a large impact for its low-cost and practicality.