Today's buildings consume nearly 40% of the total energy, and 75% of the electricity, produced by the United States. Consequently, the use of computing, sensing, and communication to make buildings smarter and greener by reducing their energy usage, costs, and footprint has become increasingly important. Many research questions arise when using computational techniques for making buildings smarter and greener. For example, how do we ``green'' buildings by integrating renewable sources, such as solar and wind, into the building energy management system? How do we reduce energy usage by automating error-prone manual tasks, such as turning down a thermostat? Unfortunately, current research is this area faces two key barriers: lack of access to energy usage data from real buildings, and an inability to conduct experimental research in live buildings due to reasons of safety and comfort. This project focuses on alleviating such barriers by building infrastructure to collect anonymous energy usage data from real homes for research purposes, as well as building a programmable mock building to enable experimentation with smart energy management algorithms.
This project will build a programmable data-driven test-bed to enable a range of research and education activities focused on sustainable buildings. Our test-bed will be comprised of two components: a data collection component to instrument a large number of real buildings of various types (e.g., single family homes, multifamily dwellings, apartments, public housing and small office buildings) to gather anonymous fine-grained actual energy usage and operational data at scale; and a programmable smart building component that comprises programmable power sources and electrical loads to enable a variety of energy management mechanisms to be evaluated in safe and repeatable manner inside a ``mock" smart building. The test-bed will enable a variety of novel research and education activities like designing intelligent load management techniques and strong user incentives for adopting such technologies; integrating renewables and energy storage into a smart building's energy management mechanisms; using learning and modeling to drive analytics of energy data to derive rich insights, as well as the design of privacy mechanisms for smart grid technologies, such as smart metering; and, using the platform for hands-on education and outreach to a broad community.
Interested researchers in this field will have access to realistic data sets and a test-bed to conduct empirical research in collaboration with local electric utility. As a result, the research enabled by the test-bed has the potential to directly impact the operations and efficiency of a real utility's grid management. There is a significant community component to share anonymized data from our instrumented buildings, as well as the design specifications with the broader research community.
