Distributed energy resources in practice: A case study analysis and \nvalidation of LBNL's customer adoption model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it