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.
Ms. Bieler is a Senior Consultant based in Denver, Colorado. She focuses on demand-side management evaluation projects, as well as strategic market assessments and planning studies. Stephanie has led several demand response market potential studies for investor-owned utilities and evaluated several of the largest DR programs in California. She has also studied and quantified the value of resilience and grid modernization investments by estimating customer interruption costs. Stephanie earned her Master’s degree at Stanford University, where she specialized in resource management, geographic information systems (GIS), and advanced statistical analysis.
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.
Andrea’s primary areas of interest are the integration of distributed energy resources, consumer energy use analysis, and load forecasting. She is experienced with statistical and machine learning computing and visualization languages, including Python, Stata, Excel, Tableau, and Power BI. Examples of recent project work include:
- Automating the collection of EV vehicle registration data to provide up to date information on EV penetration and adoption for each municipality in NY
- Developing a specialized bill calculator with AMI data to analyze customer bill impacts with and without solar, battery storage, and DR
- Developing analytics dashboard for identifying and targeting high-value for DR, battery storage, and solar + battery storage
- Analyzing water heater five-minute data to assess the ability to utilize water heater for battery storage and shift loads in response to time-of-use and demand rates
- Performing and accuracy assessment of meter-based energy savings estimates used for energy efficiency pay-for-performance and on-bill financing programs
Andrea holds a BS in Earth & Environmental Science from the University of Michigan. Before joining DSA she worked for Solar United Neighbors and USGS.
Ms. Horner joined Demand Side Analytics in 2021 after completing a Master’s in Economics at Georgia Tech., where she also served as a graduate research assistant in the School of Public Policy. Her research interests lie in using quantitative methods to assess the effectiveness and economics of different policy interventions. She is experienced in several statistical language statistical programming languages including Stata, Python, and R.
Tim Larsen is an economist who specializes in applied econometrics. He loves policy analysis and is fascinated by energy markets, their structure, regulation, and innovation. Mr. Larsen has a PhD in Economics from the University of Colorado and taught economics for several years at Colorado, Vanderbilt, and Berry College. His courses ranged from development economics to macroeconomics and his research focused on historical discrimination and corruption.
Mr. Farr joined Demand Side Analytics in 2021 after working for a large consumer products company for two years and completing a Master’s degree in Economics from the University of Texas at Austin before that. He has experience with using both Stata, R, and Python. His research interests center around using statistical modeling to understand the effectiveness of electrification and other programs.
Prior to joining Demand Side Analytics, Michael studied psychology, philosophy, and neuroscience at Washington University in St. Louis and worked in client services and product management at two Chicago-based technology companies. Michael is experienced with data wrangling, visualization, and statistical modeling, including time series forecasting and deep learning, and his top skills include Python, SQL, Tableau, Excel, and Stata. He is passionate about working collaboratively to modernize the grid, and his primary interests include distributed energy resource planning, load forecasting, and smart meter data analytics. When he’s not inside reading or cooking, Michael can be found rock climbing and exploring our nation’s beautiful parks.
Molly Sobel coordinates customer outreach and recruiting for a variety of research activities including surveys, site inspections, and stakeholder interviews. Ms. Sobel also organizes and maintains registrations with utility supply chain and procurement departments.
Mr. Charette is a quantitative analyst who enjoys cleaning and analyzing large datasets to answer clients’ questions in a straightforward manner. He has experience using SAS, R, Excel, and Stata. Anthony’s professional experience includes six years of military service, working as an EMT on an ambulance, and most recently teaching high school mathematics. He holds a BS in Computational Science andApplied Mathematics from Kennesaw State University.
Patrik Karlovic is an applied data scientist with a focus on how data can be used to better understand the relationship between energy and the environment to create actionable insights. Before coming to Demand Side Analytics, Patrik received his MS in data science from Bellevue University and went on to work for a large agricultural producer where he utilized data to identify inefficiencies within the production workflow to better improve fertilizer production and create a safer workplace for employees. Patrik has experience working with statistical programming languages, including Python, R, and Stata as well as visualization software such as Power BI. His primary interests are in how Distributed Energy Resources can be used to create a more efficient and cleaner energy grid, renewable generation forecasting, and outage analytics. When he is not in his home office in Portland, he can be found hiking the Oregon forests with his dog or at a local music concert.
John Walkington is an economist whose interests include machine learning, causal inference methods, and applied statistics for the social sciences. John graduated with a Master of Arts in Economics from The University of Texas at Austin in 2022. During his time in graduate school, John co-authored research evaluating the accuracy of The University of Texas’s epidemiological models for forecasting local COVID hospitalizations and ICU capacity. Before studying economics, he worked as a consultant for an acoustical engineering firm where he assessed noise abatement law compliance for industrial clients. John’s work at Demand Side Analytics has included evaluating demand response and emergency load reduction programs.
Matteo Zullo is a quantitative analyst at Demand Side Analytics. He is currently a Ph.D. candidate at the Georgia Institute of Technology specializing in science, technology, and education policy. He has published scientific articles that use quasi-experimental methods and machine learning, and taught undergraduate and graduate classes in Program Evaluation and Causal Inference at Georgia State University. The methodologies used in his research include panel data econometrics, synthetic controls, deep learning neural networks, genetic matching, item response theory, hierarchical linear modeling, and simulation analysis. He is fluent in state-of-the-art statistical programming software including R’s Tidyverse, Python’s NumPy and Pandas, and STATA, having received his MS in Data Analytics from the Georgia Institute of Technology and his MS in Economics from the Free University of Bozen-Bolzano. He has work experience in technology consulting with Protiviti and business modeling with Fraunhofer Society, the largest applied research institution in Europe.