Practical Method for Behind-the-Meter Solar PV Disaggregation
Bibliographic record
Abstract
The increasing adoption of rooftop solar photovoltaic (PV) generation in power distribution systems (PDS) requires innovative methods to estimate behind-the-meter (BTM) energy consumption and generation, given the widespread use of net metering. Existing approaches often rely on extensive historical data, advanced measurement infrastructure (AMI), or smart metering. In contrast, we propose a practical energy disaggregation method that operates solely on monthly net energy imports and exports, estimating hourly gross data and leveraging reference generation profiles and typical load curves for residential, commercial, and industrial consumers. A clustering algorithm is used to generate probabilistic power generation for consumer groups within a region, while the sum of the differences between registered and estimated net monthly data is minimized through an iterative process. Validated with synthetic consumers across thirteen different classifications, the proposed method effectively estimates BTM energy consumption and generation. Thus, the method provides utilities with a valuable tool for assessing prosumer behavior and understanding self-consumption patterns, helping to prevent the underestimation of actual demand during PV generation periods while supporting grid operation and planning.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".