Distributed energy resources in practice: A case study analysis and \nvalidation of LBNL's customer adoption model
Bibliographic record
Abstract
This report describes a Berkeley Lab effort to model the economics and operation of small-scale (<500 kW) on-site electricity generators based on real-world installations at several example customer sites. This work builds upon the previous development of the Distributed Energy Resource Customer Adoption Model (DER-CAM), a tool designed to find the optimal combination of installed equipment, and idealized operating schedule, that would minimize the site's energy bills, given performance and cost data on available DER technologies, utility tariffs, and site electrical and thermal loads over a historic test period, usually a recent year. This study offered the first opportunity to apply DER-CAM in a real-world setting and evaluate its modeling results. DER-CAM has three possible applications: first, it can be used to guide choices of equipment at specific sites, or provide general solutions for example sites and propose good choices for sites with similar circumstances; second, it can additionally provide the basis for the operations of installed on-site generation; and third, it can be used to assess the market potential of technologies by anticipating which kinds of customers might find various technologies attractive. A list of approximately 90 DER candidate sites was compiled and each site's DER characteristics and their willingness to volunteer information was assessed, producing detailed information on about 15 sites of which five sites were analyzed in depth. The five sites were not intended to provide a random sample, rather they were chosen to provide some diversity of business activity, geography, and technology. More importantly, they were chosen in the hope of finding examples of true business decisions made based on somewhat sophisticated analyses, and pilot or demonstration projects were avoided. Information on the benefits and pitfalls of implementing a DER system was also presented from an additional ten sites including agriculture, education, health care, airport, and manufacturing facilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".