Toward Community Generation: Energy Simulation and Performance Evaluation of Multi-family Solar PV Settings for Energy-efficient Homes in Edmonton, Canada
Bibliographic record
Abstract
The integration of renewable micro-generation systems into residential buildings, particularly solar photovoltaic (PV) distributed energy generation, is emerging rapidly as an effective method of mitigating the housing impact on greenhouse gas emissions. However, the application of solar PV micro-generation is confronted with several challenges: (a) the average system self-consumption does not exceed 25% in cold-climate regions; (b) most of the energy generated during daytime, peaking in the summer, is exported to the grid; and (c) rebates from the surplus generated energy exported to the grid are at a lesser rate than that of the imported energy. Due to relatively poor economics paralleled with the solar PV application, governments and policy makers envision the value of considering the integration of renewable energy sources at the community level rather than individual behind-the-meter applications, since this strategy can leverage the system self-consumption and increase its social impacts and economics. In this regard, this research aims to simulate and compare the overall performance of two scenarios of a sustainable community of 42 townhouse units. In the first scenario, each unit is connected to a behind-the-meter solar PV system of 3.3-kWp. In the second scenario, all units are connected to a large 140-kWp solar PV system. Historical data from one typical house has been collected (ongoing since 2015). Monte Carlo simulation technique is applied to ensure the stochasticity of the diverse household users. The hourly energy consumption and generation data is simulated using Simphony.NET® simulation engine based on the real-time data collected in Edmonton, Canada. Then, the load-match is identified as well as grid interaction indicators and system economics resulting from both scenarios. Results indicate that the application of community generation can significantly mitigate the imported and exported energy compared with individual behind-the-meter system generation due to the improved system self-consumption.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".