California DREAMing: the design of residential demand responsive technology with people in mind
This study developed several technologies to enable demand response for residential electricity customers in order to reduce peak consumption. First, along with a team of students and professors, I designed and tested the Demand Response Electrical Appliance Manager (DREAM). This wireless network of sensors, actuators, and controller with a user interface intelligently controls a residential heating and cooling system and informs people of their energy usage. Secondly, I evaluated machine-learning to predict a person’s seasonal temperature preferences by analyzing existing data from office workers. The third part of the research developed an algorithm that generated temperature setpoints based on outdoor temperature and compared simulated energy use with these versus the default setpoints of a programmable thermostat. Finally, I developed and tested a user interface for a thermostat and in-home energy display. This part of the study tested the effects of both energy versus price information and the context of sponsorship on the behavior of subjects, and also surveyed subjects on the usefulness of various displays.
The wireless network succeeded in providing detailed data to enable an intelligent controller and provide feedback to the users. The learning algorithm showed mixed results. The adaptive temperature setpoints saved energy in both annual and summertime simulations. The context in which I introduced the DREAM interface affected behavior, but the type of information displayed did not. The subjects responded that appliance-level feedback and tools that provided choices would be useful in a dynamic tariff environment.