Jesse Smith is an applied statistician whose work is centered around estimating the impacts of demand side interventions to alter the way homes and businesses use energy. Over the last decade in the energy industry, Jesse has been involved in the evaluation, measurement and verification (EM&V) of a wide variety of demand response, dynamic pricing, and energy efficiency programs implemented by electric and gas utilities across North America. He specializes in statistical analysis of energy usage data, sampling, experimental design, and benefit cost modeling and is comfortable working in Stata, SAS, SQL, and Excel. He received a BS in Psychology from the University of North Carolina at Chapel Hill and a MS degree in Applied Statistics from Kennesaw State University. Prior to founding Demand Side Analytics in 2016, Jesse worked as a managing consultant at Nexant and as a load research analyst for GoodCents Solutions where he performed statistical analyses of the energy and demand savings of a number of direct load control and energy efficiency programs for client utilities.
Josh Bode specializes in advanced applications of data analytics using large volumes of hourly and sub-hourly data for evaluation, valuation, planning and forecasting in the energy sector. He has led over 50 studies including some of the first innovations and largest applications of smart meter and SCADA data analytics in topics as varied as:
- Impact evaluations of time varying pricing, demand response, behavioral programs, and energy efficiency programs;
- Location specific probabilistic forecasting and planning methods, including locations specific T&D marginal costs;
- DER valuation and cost-effectiveness;
- Market potential studies of DER’s including distribution level micro-potential studies, and
- Value based targeting analytics
As part of these studies he has worked with smart meter data for millions of residential and small and medium businesses and with the full population of large customers from numerous utilities. He also has applied experience with large scale transmission level, substation, and distribution circuit feeder hourly data from multiple utilities, including PG&E, ConEdison, O&R, Central Hudson, NYSEG, RG&E, and National Grid (Rhode Island). Most recently, he has worked on projects designed to align distributed energy resources with grid value and in developing location specific, probabilistic forecasts and T&D marginal costs. He received a BS in Business Economics from Willamette University and a Master’s degree in Public Policy from the University of California, Berkeley.
Ms. Lemarchand’s work has focused an array of Distributed Energy Resources (DERs) from solar photo-voltaic to electric vehicles to demand response all in the context of Non Wires Alternative assessment and sustainable DER rate and program design. This work has included DER valuation, market and empirical research design and analysis to align DER program and rate design with corporate goals, and strategic Utility of the Future road mapping for utilities seeking to incorporate DERs into distribution planning and corporate strategy. Last year, Alana assisted Central Hudson’s development of a Distributed System Implementation Plan (DSIP) including granular load and DER forecasting. Alana also co-authored a paper in which DER valuation approaches were recently laid out, SEPA Beyond the Meter: Addressing the Locational Valuation Challenge for DERs. More recently she has supported design and planning of a DER technology agnostic prices-to-devices pilot.
Prior to shifting her consulting focus to the utility industry, Ms. Lemarchand was a Senior Consultant at Simon-Kucher & Partners, a pricing strategy consultancy, where she managed project teams focused on developing pricing strategies for a diverse array of companies including consumer internet start-ups, providers of small business services, and enterprise software giants, among others. Ms. Lemarchand holds a B.S. in Environmental Economics and Policy from the University of California, Berkeley, where she graduated as the top student in her class and was selected as the commencement speaker.
Ms. Ciccone has extensive expertise with analysis of smart meter data, rate design, potential studies, and impact evaluation for pricing, demand response, and behavioral programs. Some recent examples of her work include:
- Analysis of revenue neutral cost-based rate design for over 2,000 time varying, demand and demand subscription rates including impacts on customer bills and volatility.
- Analyzing energy savings for behavioral programs for Seattle City Light
- Implementing the analysis for the California Statewide Non-residential Critical Peak Pricing and the California Baseline Interruptible Program for multiple years
- Estimating impacts using smart meter and interval data for utility programs at SDG&E and SCE.
- Contributing to the pricing analysis and development of adoption propensity scores for 12 million customers (both residential and non-residential) as part of the California Demand Response Potential Study
- Implementing baseline accuracy studies for the California ISO and Commonwealth Edison. The CAISO baseline accuracy study included testing of accuracy for over 5,000 baseline rules using smart meter and interval data from approximately 500,000 DR program participants for over 10 programs at PG&E, SCE, and SDG&E.
Prior to joining Demand Side Analytics, Adriana Ciccone worked at Nexant and Procter & Gamble. She attended MIT for college, where she double majored in Operations Research and Materials Science & Engineering. She also holds a MS degree in Environmental Science and Policy from the University of Chicago. Her graduate studies included program evaluation, computer science, and energy economics and policy.
Mr. Morris is an applied statistician with wide exposure to evaluation with interval data. Since joining Demand Side Analytics, he has contributed to a variety of energy efficiency and demand response evaluation projects and supported the development of the IPMVP Option C uncertainty guidelines for the Efficiency Valuation Organization. He is versed in three of the most prominent statistical packages – R, SAS, and Stata – and has experience translating code from one language to another. He attended the University of Georgia, where he received the Hollingsworth Award for excellence in undergraduate mathematics and received an MS in statistics. He currently teaches statistics at Kennesaw State University.
Examples of recent projects include:
- Analysis of NY vehicle registration data (11.9M vehicles) to develop granular forecasts of electric vehicle adoption, including assessing the impact on hourly (8,760) substation load forecasts. As part of this project, he analyzed the rate of vehicle turnover, estimated the historical penetration over time of electrified vehicles, estimated innovation diffusion curves, estimated adoption propensity scores over time, developed 8760 hourly EV load shapes, and assessed the impact of electric vehicle adoption on substation loads.
- Analysis of peak reduction impacts from smart thermostats. As part of this project, he used 5-minute thermostat run-time data to estimate summer and winter peak load reduction impacts. Helped develop an automated tool that implements the analysis within 24 hours of each event and draft a report that is sent to the client.
- Audit analysis of seven large scale randomized control trials for home energy reports behavioral interventions in Pennsylvania, designed to encourage energy conservation of electricity. In total, the randomized control trial includes 1.2 million participants and hundreds of thousands of customers in the control groups.
Ms. Totten’s primary areas of interest are applied analysis and impact evaluation. She has extensive experience with several statistical computing languages and has held research teaching positions for Econometrics and Statistical Methods.
Examples of recent projects include:
- Impact evaluation of smart thermostat electric and gas energy savings. The study relied on an analysis of 200 treatment and control customers using electric smart meter and gas billing data.
- Estimation of impacts from smart thermostat algorithms designed to produce energy savings. As part of this project she analyzed 5-minute run time data from over 3,000 thermostats. The study relied on an alternating treatment design, where the algorithm was on two out of three days over the course of the year, allowing for estimation of energy use with and without the algorithm.
- Implemented the analysis of impacts from default dynamic pricing rates for non-residential customers. The analysis included 3 years of hourly data for over 120,000 small business accounts and relied on development of a control group using propensity score matching. The impacts were estimated using a difference-in-differences panel regression.
Mark’s primary experience is in electricity market modeling for applications including asset valuation, transaction work, and integrated resource planning for government, utility, and private sector clients. He has also worked on technology cost projections, capacity market design, and market concentration indices in the natural gas market. He is experienced with statistical computing and visualization languages including Stata, R, and Excel, as well as the Aurora electricity market forecasting software. Mark holds a Bachelor of Arts in Economics from Georgetown University.
Andrea’s primary areas of interest are integration of distributed energy resources, consumer energy use analysis, and load forecasting. She has worked on a wide range of projects including residential solar market evaluation and load forecasting using smart meter data. She is experienced with statistical and machine learning computing and visualization languages including Python, Stata, Excel and Tableau.
Ms. Burley’s primary interests include program impact evaluation, load forecasting, and benefit-cost analysis. Previously, she worked at an applied economic consulting firm focused on pricing and subscription strategy for print and digital media. She is experienced in several statistical language statistical programming languages including Stata, R, Python, and Excel.