The final comments on daily and billing period methods have been received. The new, finalized specifications are currently being updated you can track the final on GitHub Issue #82. Participants can review the final specifications and track any issues that fed into the final specification prior to their publication at this site.
The CalTRACK methods are equipped to normalize for weather’s effect on a building’s energy consumption. The methods become unreliable when a building’s energy consumption patterns are instead correlated with non-weather variables. For example, irrigation pumps are used on an agricultural cycle rather than in response to temperature changes. It is reasonable to expect higher energy consumption during the growing season and an analyst may suggest re-specifying the model to control for these seasonal variations. Without modification, existing billing and daily CalTRACK models cannot accommodate these nuanced cases. For this reason, buildings that are not well-specified by the CalTRACK model should be identified and removed from portfolios because they have significant effects on portfolio uncertainty. This issue can be tracked on GitHub Issue #71.
To evaluate a building’s qualification status, a metric and a thresholds should be defined. After empirical testing, the recommended metric for CalTRACK 2.0 is CV(RMSE).
- The properties of the CV(RMSE) metric penalize buildings with outlier energy use. Due to their significant effect on portfolio uncertainty, it is recommended that buildings with outlier energy use are eliminated from portfolios. The CV(RMSE) metric makes removing outlier buildings from portfolios more important.
- CV(RMSE) is not sensitive to close to zero individual usage values. This makes it more robust than MAPE and similar metrics.
Building type can be a useful identifier for aggregators to determine a building’s suitability for a portfolio. In figure 1, the relationship between energy consumption and CV(RMSE) is measured by building type. The size of each dot corresponds with the number of meters per building type.
Due to differences in model quality, portfolio size, and building type between aggregator datasets, it is difficult to establish a universally applicable building-level CV(RMSE) cut-off. The graphs below visualize the relationship between building-level CV(RMSE), portfolio uncertainty, and building attrition. The various graphs show these relationships with different building types and portfolio sizes. While analyzing the graphs, consider that procurers and aggregators tend to be more concerned with portfolio-level uncertainty and building attrition than the building-level CV(RMSE), especially for pay-for-performance programs and Non-Wires Alternatives procurements.
We are recommending that the specific building eligibility requirements be generally left to the procurer, who can set the requirements that align best with their goals for a procurement, provided these are specified clearly upfront. CalTRACK can provide general guidelines as follows.
- For use cases where confidence in portfolio-level performance is required (e.g. aggregator-driven pay-for-performance, non-wires alternatives (NWA) procurements), we recommend using a permissive building-level CVRMSE threshold (100% is recommended), but requiring that a portfolio-level metric be respected (e.g. weighted mean CVRMSE or portfolio fractional savings uncertainty). The portfolio-level threshold will be a policy decision and may differ depending on the use case (e.g. NWA procurement may require less than 15% uncertainty, regular pay-for-performance program may require 25% to align with ASHRAE Guideline 14 etc.)
- For use cases where confidence in individual building results is required (e.g. customer-facing performance based incentives), ASHRAE Guideline 14 thresholds may be used.
Normalized Metered Energy Consumption Draft Guidance CPUC
ASHRAE Guideline 14
- Comments on Recommendations for Building Qualifications
- Suggestions and recommendations for hourly models
- Revisiting other requirements in terms of hourly models
- Tests for hourly models