IMPACT ANALYSIS OF RESIDENTIAL PHOTOVOLTAIC SYSTEM USING HOURLY GREENHOUSE GAS EMISSION DATA FROM ELECTRICITY GENERATION
Bibliographic record
Abstract
In this study, seasonal greenhouse gas (GHG) emission factors were developed to realize the true CO2 reduction potential of a small scale renewable energy technology. The new hourly greenhouse gas emission factors (NHGHGIFA), from Gordon and Fung (2007) based on hour-by-hour demand of electricity in Ontario, and the Greenhouse Gas Intensity Factor (GHGIFM) from Ontario Power Generation (2004), were applied to operational data from a 5kW photovoltaic (PV) system obtained from Good et. al (2006). Analysis of results based on the NHGHGIFA, yielded two individual emission factors comprising both summer and winter months. These factors, namely the winter greenhouse gas intensity factor (WGHGIF) and summer greenhouse gas intensity factor (SGHGIF), were found to be 274 g CO2/kWh and 191 g CO2/kWh respectively and represent CO2 reduction potential by the PV system within Ontario’s energy mix. The large variance between these figures is attributed to the increased environmental CO2 burden observed in the province of Ontario as a result of fossil-fuel plant operation during winter months as stated in Gordon (2006). A secondary analysis using the GHGIFM was performed to determine the hourly CO2 reductions possible through direct fossil to PV substitution. The use of regionally specific climate-modeled factors such as those identified, allow for a more accurate representation of the benefits associated with GHG reducing technologies, such as PV cell systems. In addition, a neural network (NN) model was successfully developed in order to predict the hour-by-hour electricity demand for Ontario using environmental factors with a predictive performance of 96 % accuracy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".