Solar infrared radiation towards building energy efficiency: measurement, data, and modeling
Bibliographic record
Abstract
With the recent discoveries and engineering solutions emerging in nanomaterials and nanostructures, independent band modulation of solar radiation on building envelopes, including glazing systems, has become increasingly viable as a potential means of improving building energy savings and indoor visual comfort. However, when it comes to the prediction of these new materials’ potential energy performance in buildings, most studies utilize a simple solar irradiance (e.g., global horizontal solar irradiance, direct beam solar irradiance) or a rough estimation of solar infrared (e.g., 50% solar irradiance) as input, which may cause significant errors. Consequently, there is a pressing need for reliable performance estimations of the solar infrared control and response at the building’s scale. To assess this, we need a solar spectral irradiance model, or at least a wideband (visible or infrared) solar irradiance model, as input. To develop this new type of model, one needs to understand the modeling-related key elements, including available solar spectral irradiance datasets, data collection methods, and modeling techniques. As such, this paper reviews the current major measurement methods and tools used in collecting solar spectral irradiance data with a focus on the solar infrared region, identifies the available related resources and datasets that particularly encompass the solar spectral irradiance data with a sufficient wavelength range, and studies existing solar irradiation modeling techniques for building simulations. These investigations will then form the background and backbone for a study scheme of solar infrared radiation modeling and indicate future research paths and opportunities.
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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 | 0.000 | 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".