Bibliographic record
Abstract
The aim of this research is to increase the assessment ability of solar energy utilization and planning support for clusters of different types of buildings in a mixed-use community. Particular focus will be placed on the analysis of community-based modeling, mapping and forecasting of solar potentials on the rooftops of buildings. New systems and methodologies with appropriate level of detail at a lower computational time are needed to accurately model, estimate and map solar energy potential at a high spatiotemporal resolution. To accomplish this goal and to develop an integrated solution, the assessment ability was investigated using two different types of studies: (1) 3D GIS modeling of a solar energy community, and (2) benchmarking of solar PV radiation software tools. A 3D GIS modeling and mapping approach was developed to assess community solar energy potential. A model was created in ESRI ArcGIS, to efficiently compute and iterate the hourly solar modeling and mapping process over a simulated year. The methodology was tested on a case study area located in southern Ontario, where two different 3D models of the site plan were analyzed. The accuracy of the work depended on the resolution and sky size of the input model. An assessment of solar simulation software tools was performed to evaluate their strengths and weaknesses for performing analysis in the PV modeling process. The software tools assessed were HelioScope, PVsyst, PV*SOL, Archelios, EnergyPlus, and System Advisor Model (SAM). The performance of the software tools were assessed based upon their accuracy in simulation performance against measured data, and the comparison of their physical functions and capabilities. A case study near London, Ontario with an 8.745kWp PV system installation was selected for analysis, and EnergyPlus was found to have predictions closest to measured data, ranging from -0.6% to 3.6% accuracy. Based upon the GIS study and the evaluation of the six solar software tools, recommendations for the development of a future application to couple GIS with the internal submodels of the software tools were made to create the ideal tool for 3D modeling and mapping of solar PV potential.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".