The electric grid has begun a profound transition from primarily using carbon-intensive energy to instead using carbon-free renewable energy. In parallel, smart meters and other sensors are now providing us unparalleled visibility into the energy-efficiency of building and grid operations. Researchers are actively using building and grid energy data from these sensors to develop analytics techniques, e.g., using machine learning, that can improve energy-efficiency and facilitate the energy transition. Unfortunately, much of this research ignores the impact of these analytics on equity. That is, while current data analytics techniques may accurately identify energy-inefficiencies, they generally do not contextualize the underlying reasons for these inefficiencies. For example, data analytics that identify the most energy-inefficient homes might motivate new programs that target these homes for subsidies to improve energy-efficiency. However, the most energy-inefficient homes might also correlate with those with the highest income that have less need for subsidies, and engage in the most energy wasteful behavior. In contrast, the most energy-efficient homes might be the homes that can least afford to waste (or even use) energy. In this paper, we use an example from recent research to illustrate the inequity of state-of-the-art energy analytics, and argue that energy analytics research should elevate equity to a first-class concern.
Journal or Book Title
BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation