| Electrical utilities worldwide are exploring "demand response" programs to reduce electricity consumption during peak periods. Californian electrical utilities would like to pass the higher cost of peak demand to customers to offset costs, increase reliability, and reduce peak consumption. Variable pricing strategies require technology to communicate a dynamic price to customers and respond to that price. However, evidence from thermostat and energy display studies as well as research regarding energy-saving behaviors suggests that devices cannot effect residential demand response without the sanction and participation of people.;This study developed several technologies to promote or enable residential demand response. 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 provides information to intelligently control a residential heating and cooling system and to inform people of their energy usage. We tested the system with computer simulation and in the laboratory and field. Secondly, as part of my contribution to the team, 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 involved developing an algorithm that generated temperature setpoints based on outdoor temperature. My study compared the simulated energy use using these setpoints to that using the setpoints of a programmable thermostat. Finally, I developed and tested a user interface for a thermostat and in-home energy display. This research tested the effects of both energy versus price information and the context of sponsorship on the behavior of subjects. I 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. |
