Modeled Estimates of Solar Direct Normal Irradiance and Diffuse Horizontal Irradiance in Different Terrestrial Locations
Bibliographic record
Abstract
The transformation of solar energy into electricity is starting to impact to overall worldwide energy production mix. Photovoltaic-generated electricity can play a significant role in minimizing the use of non-renewable energy sources. Sunlight consists of three main components: global horizontal irradiance (GHI), direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI). Typically, these components are measured using specialized instruments in order to study solar radiation at any location. However, these measurements are not always available, especially in the case of the DNI and DHI components of sunlight. Consequently, many models have been developed to estimate these components from available GHI data. These models have their own merits. For this thesis, solar radiation data collected at four locations have been analyzed. The data come from Al-Hanakiyah (Saudi Arabia), Boulder (U.S.), Ma’an (Jordan), and Ottawa (Canada). The BRL, Reindl*, DISC, and Perez models have been used to estimate DNI and DHI data from the experimentally measured GHI data. The findings show that the Reindl* and Perez model outcomes offered similar accuracy of computing DNI and DHI values when comparing with detailed experimental data for Al-Hanakiyah and Ma’an. For Boulder, the Perez and BRL models have similar estimation abilities of DHI values and the DISC and Perez models are better estimators of DNI. The Reindl* model performs better when modeling DHI and DNI for Ottawa data. The BRL and DISC models show similar metrics error analyses, except in the case of the Ma’an location where the BRL model shows high error metrics values in terms of MAE, RMSE, and standard deviation (σ). The Boulder and Ottawa locations datasets were not complete and affected the outcomes with regards to the model performance metrics. Moreover, the metrics show very high, unreasonable values in terms of RMSE and σ. It is advised that a global model be developed by collecting data from many locations as a way to help minimize the error between the actual and modeled values since the current models have their own limitations. Availability of multi-year data, parameters such as albedo and aerosols, and one minute to hourly time steps data could help minimize the error between measured and modeled data. In addition to having accurate data, analysis of spectral data is important to evaluate their impact on solar technologies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".