Demand Response and Smart Grid
"Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States." Energy Research & Social Science 70 (2020). .
"Spillover as a cause of bias in baseline evaluation methods for demand response programs." Applied Energy 250 (2019) 344 - 357. .
Demand Response Advanced Controls Framework and Assessment of Enabling Technology Costs. 2017. LBNL-2001044. .
"Predicting Baseline for Analysis of Electricity Pricing." International Journal of Big Data Intelligence (IJBDI) Vol. 5.No. 1/2, 2018 (2017) 3-20. .
Federal/State Jurisdictional Split: Implications for Emerging Electricity Technologies. 2016. LBNL-1006675. .
"Go for the Silver? Evidence from field studies quantifying the difference in evaluation results between “gold standard” randomized controlled trial methods versus quasi-experimental methods." 2016 ACEEE Summer Study on Energy Efficiency in Buildings 2016. .
"Time Will Tell: Using Smart Meter Time Series Data to Derive Household Features and Explain Heter ogeneity in Pricing Programs." 2016 ACEEE Summer Study on Energy Efficiency in Buildings 2016. .
Time-of-Use as a Default Rate for Residential Customers: Issues and Insights. 2016. LBNL-1005704. .
"Considerations for State Regulators and Policymakers in a Post-FERC Order 745 World." ElectricityPolicy.com (2015). LBNL-6977E. .
Insights from Smart Meters: Ramp-up, dependability, and short-term persistence of savings from Home Energy Reports. State and Local Energy Efficiency Action Network (SEE Action), 2015. LBNL-182265. .