Field Data-Driven Discrete-Event Simulation of Residential PV/ Energy Storage Systems in Cold Climate Regions
Bibliographic record
Abstract
This paper presents a field data-driven simulation model for PV and battery systems in residential buildings. The in-creased electricity demand in buildings, particularly in morning and evening peak periods, has increased the need for optimized on-site renewable energy utilization. A battery system's output de-pends on various parameters, including occupant energy use be-havior, load profile, operation mode, state of charge, battery man-agement system programming, and weather conditions. This pa-per addresses the gap by proposing a discrete-event simulation model based on field data collected from 75 homes in Atlantic Can-ada. The model replicates the performance of PV and battery sys-tems under time-based control mode. Validation of the model demonstrates promising results. The evidence-based model can in-form utility companies, ratepayers, and stakeholders about the costs and benefits of implementing battery energy storage systems and provides insights for developing decarbonization plans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".