​Tuesday, December 8th, 2020
1:00 PM Pacific Time

Who is it for?
Utilities that are struggling with planning for exogenous impacts on energy consumption.

Register
COVID-19 has upended the energy efficiency and demand flexibility industry’s assumptions about energy use and rendered traditional Measurement and Verification (M&V) all but useless. Longer-term trends of decarbonization, electrification, and increased renewable, intermittent generation and other exogenous factors create similar challenges to understanding and valuing flexibility interventions.

In this webinar, Recurve CEO Matt Golden and Director of Policy & Emerging Markets Carmen Best explain how the new GRIDmeter™ project successfully used privacy protected utility data to identify comparison groups in order to differentiate the way in which COVID impacted different kinds of commercial and residential energy consumers -- and how utilities can apply these insights immediately to understand the impacts of COVID and other exogenous factors on their own programs. 

Alice Havenar-Daughton of MCE will be joining the discussion to talk about the value of this analysis to the evolution of their energy programs.
​
Register here. 

Six months ago, with support from the Department of Energy and help from MCE and other partners, Recurve initiated a working group to develop new open source Measurement and Verification (M&V) tools for an era in which energy consumption is defined by uncertainty. ​

Acknowledgments​
​These methods and open-source code was developed based upon funding from the U.S. Department of Energy, through the National Renewable Energy Laboratory. Recurve would also like to thank MCE for supporting this project by providing secure access to data. 
​
Finally, Recurve thanks the members of the Comparison Groups Working Group, who devoted their time and effort to listening, reviewing, and providing feedback throughout the research and development of the methods and recommendations in this report.

Program tracking with annual savings calculations and portfolios of 3,000+ heterogeneous buildings. Random sample of 3,000+ non-participant buildings from eligible population

Program tracking with annual savings calculation with portfolios of 1,000+ relatively homogeneous buildings.  Random sample of 3,000+ non-participants from targeted population

Program tracking with marginal hourly savings calculations with smaller numbers of homogeneous buildings. Stratified sample of 3,000+ non participants from targeted population

 Savings reconciliation after enrollment has closed and full treatment population is known, especially needed where program enrollments are substantially different than comparison pool. Stratified sample that maximizes sample size relative to error between treated and non-treated groups

●Would a comparison group that matches the baseline usage pattern of a treatment group start to exhibit bias over time that would negate some of the savings of the treatment group?
●We know that there might be self-selection issues that might need to be addressed during the selection of the comparison group
●But we don’t know what the variability of the bias might be and we don’t know whether or not it might get worse under conditions of large-scale energy changes, such as during COVID

●Simulate the selection of treatment and comparison groups for a program
●Target a subset of high peak load users, similar to what might done for a program that is focused on peak load reduction
●Select a variety of sizes of “treatment” and “comparison” groups from the larger targeted population that can be compared against each other

 Bias is inevitable in comparison groups due to factors beyond our ability to control for

2) Larger groups will reduce bias, but treatment group may still diverge from comparison group over time

3) Need for more systematic approach to determine optimal comparison and treatment group sizes

Stratification is about binning

●What parameters?
●Equal distance or population?
●How many bins?
●How many parameters?
●In what order of priority?
●Should there be an order?
●How do you gauge success?


Stratified Sampling: What are the Constraints?
The only option in resampling is the elimination of meters.

Two main constraints:

1.The size of the comparison pool
2.The number of meters needed in the final sample

Standardizing Stratification: Dealing with Multiple Parameters

Multiple parameters can generate a more representative sample

●Complexity: Stratifying the first parameter and then moving on to the next will inherently change the first
               ○Solution = Simultaneous 2D or nD binning
●How should the individual parameters be prioritized and binned?
               ○Solution = An optimal scheme should be determined via                    algorithm.
●How do you gauge success?


Video of the August 28, 2020 Meeting: Comparison WG VIDEO - 2020-08-28
Slides from the August 28, 2020 Meeting (cumulative): Comparison WG SLIDES - 2020-08-28
Chat Record from the August 28, 2020 Meeting: Comparison WG Chat - 2020-08-28

The “Difference of Differences” Calculation

• Step 1: Create baseline period model for treatment group and project that model as a counterfactual into the reporting period (Counterfactual_Treatment)
• Step 2: Create baseline period model for comparison group and project forward as a counterfactual (Counterfactual_Comparison)
• Step 3: Calculate savings as:
  
  

Savings = 
(Counterfactual_Treatment - Observed_Treatment) - (Counterfactual_Comparison - Observed_Comparison)

Video of the August 14, 2020 Meeting: Comparison WG VIDEO - 2020-08-14

Slides from the August 14, 2020 Meeting (cumulative): Comparison WG SLIDES - 2020-08-14

Chat Record from the August 14, 2020 Meeting: Comparison WG Chat - 2020-08-14

For most comparison pools, stratify on up to 3 parameters

1. Annual Consumption
2. Percent usage change from COVID
3. 3. A parameter of relevance to the program (likely a normalized metric)

Examples of normalized electric features

% Heating kWh

% Baseload kWh

% Summer Peak kWh

Video of the July 31, 2020 Meeting: Comparison WG VIDEO - 2020-07-31
Slides from the July 31, 2020 Meeting (cumulative): Comparison WG SLIDES - 2020-07-31
Chat Record from the July 31, 2020 Meeting: Comparison WG Chat - 2020-07-31

Goal: Develop and Demonstrate successful implementation of stratified sampling

• What parameters can/should be used
• Develop codebase and test/refine
• Multidimensional stratification

Question: What does it take (binning schemes) to create equivalency between treatment and comparison?









Video of the July 17, 2020 Meeting: Comparison WG VIDEO - 2020-07-17

Slides from the July 17, 2020 Meeting (cumulative): Comparison WG SLIDES - 2020-07-17

Chat Record from the July 17, 2020 Meeting: Comparison WG Chat - 2020-07-17

• Assumption: There is a singular event in a building that marks the beginning of a new pattern of energy consumption.
• We want to be able to calculate the difference between this new pattern of energy use and the energy use that would have occurred if the old pattern was still in place.

• Confusing term: baseline
  • Baseline can refer to the chronological time frame corresponding to the historical energy consumption of a building. For example, we might say “12 month baseline” and refer to the time period prior to the new pattern of energy use.
  • Baseline can also refer to the “counterfactual.” This is the estimate of how much energy would have been consumed at any given point if no changes had occurred in the building’s energy use pattern. For example, when we calculate savings, we subtract actual energy use from “baseline” energy use. In this case, we are subtracting from a calculated counterfactual rather than the actual historical consumption.
• Confusing term: normalize
  
  
  • Statistical term versus a weather term
    
    

• In California, the policy term of art used to describe meter-based programs is NMEC - Normalized Metered Energy Consumption.
• Normalized (statistical)
  • Identification of a key predictive factor that correlates well with changes in energy consumption
    • Outdoor weather conditions (dry bulb temp, wet bulb temp, wind, cloud cover)
    • Indoor conditions (occupancy, production, certain end uses)
  • What is the minimum set of data that we have available across all buildings that will allow for consistent, replicable analysis?
    • Outdoor temperature (dry bulb)
    • Indoor building state (occupied/unoccupied by inference)
      
      
  • Normalization means that we use a particular set of exogenous conditions to calculate a counterfactual energy consumption value
    • E.g., When the daily average temperature is 80, this building will use 27 kWh of electricity on average.

Video of the June 05, 2020 Meeting: Comparison WG VIDEO - 2020-06-05

Slides from the June 05, 2020 Meeting (cumulative): Comparison WG SLIDES - 2020-06-05

Chat Record from the June 05, 2020 Meeting: Comparison WG Chat - 2020-06-05