Historical Insights for Electricity Transition Scenarios in California and Flexible Energy Demand Modeling for Residential Air Conditioning with Improved Behavioral Specificity
Improving ability and tools to understand, anticipate, and shape energy use transitions in California
The research team convened in-depth discussions with members from the CEC's Demand Analysis Office, Supply Analysis Office, Building Standards Office, and Research Division as they worked to refine their flexible residential air conditioning demand forecast model ("SIMSAND"). SIMSAND, a prototype model to enable exploration of impacts of a number of human factors on residential air conditioning demand, was enhanced to have the flexibility to explore scenarios related to technology policy, adoption, and projected climate change. Through an agreement with Canadian smart thermostat manufacturer EcoBee, researchers obtained a very large data set of thermostat settings from 'Donate Your Data' volunteer households. Researchers submitted a final report for publication and presented their work at the 2019 Behavior, Energy, and Climate Change Conference (BECC).View Final Report
Analysis of quantitative and qualitative data sheds light on past energy technology transitions, planned and unplanned. The research team's analysis of these transition histories provided examples, principles and insights that can be used in future planning. Leveraging insights from historical technology transitions, the research team designed and tested a flexible, agent-based modeling platform ("SIMSAND") that enables researchers, utilities, and CEC demand modeling and forecasting staff to draw upon a wealth of empirical data as well as projected climate data to simulate dynamic residential demands for air conditioning. This model served as a proof of concept that might later be broadened to other energy uses and demand sectors. The model also illustrated the power of providing a platform that can draw on a variety of data streams available to illuminate energy consumption and potential transition trajectories, rather than representing key parameters as simple averages.
The study developed a rigorous model that enables exploration of human dimensions that could hinder or enable the technologies that are essential in the transition to a deeply decarbonized electricity system.