Socioeconomic-geographic Intelligence for Grid Net-load Analytics and Learning (SIGNAL)

The Socioeconomic-geographic Intelligence for Grid Net-load Analytics and Learning (SIGNAL) project, under solicitation EPC-26-002, is an agreement funded by the California Energy Commission to develop advanced multi-horizon net-load forecasting models. Utilizing actionable intelligence, the project focuses on predicting Behind-the-Meter distributed energy resource behavior for electric vehicles, battery energy storage systems, and solar photovoltaics. The primary objective is to enhance short-term forecasting accuracy during high-variability events to improve grid resilience and flexibility in California, ultimately providing cost savings for ratepayers through optimized demand response and load shifting. Conducted by UC Irvine and based in Laguna Woods, California, the project is managed by Alejandra Rios and Principal Investigator Guann-Pyng Li. The initiative has been granted a categorical exemption under the California Environmental Quality Act, as the scope of work is limited to basic data collection and research regarding energy resources and is determined to have no significant environmental impact.