Preliminary Assessment of a Weather Forecast Tool for Building Operation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although the potential of model predictive control (MPC) for the operation of buildings is widely recognized, as of today its adoption has been rather limited. This is partly due to the lack of user-friendly software tools for MPC, such as tools to facilitate the incorporation of forecast information in building automation systems. In view of this, CanmetENERGY, a research centre of Natural Resources Canada, has developed CanMETEO, a software tool free of charge aimed at obtaining weather forecast data and make it available in a useful and practical format for building operators. CanMETEO, which was released officially in August 2017, uses raw data produced by the Meterological Service of Environment Canada. This data, with high spatial resolution (e.g., 2 km x 2 km grids, and even denser for urban areas) enables the possibility of obtaining forecasts for very specific locations in the Canadian territory. Hundreds of weather variables (such as temperature, humidity, wind speed, cloud cover, among many others) are available for each point, which can be selected by the user via a graphical interface. The data is converted from GRIB files (a standard binary format used by meteorologists) into comma-separated value (CSV) files, which can be easily accessed. New forecasts become available every 6 hours, with a prediction horizon of 48 hours at hourly time steps; the retrieval of new weather forecasts can be setup in order to be performed automatically. These continuously updated CSV files may then be easily incorporated into building operation algorithms or simple optimization routines. Once the basic variables are obtained, post-processing calculations are applied in order to estimate solar irradiance on any given plane required by the user, for example, building façades and building-integrated photovoltaic panels. This feature also makes it possible to estimate the effect of solar gains on the thermal response of a building, and to estimate the output of photovoltaic panels. A preliminary evaluation of the tool, based on on-site measurements, is presented in this paper. It is expected that CanMETEO (currently used by Canadian research centre and universities) will provide one further step to the widespread adoption of predictive control as a viable, popular solution in building operation.
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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 it