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Record W2904512851

Preliminary Assessment of a Weather Forecast Tool for Building Operation

2018· article· en· W2904512851 on OpenAlex
José A. Candanedo, Jean-Marc Hardy, Étienne Saloux, Radu Platon, Vahid Raissi-Dehkordi, Alexandre Côté

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.

fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePurdue e-Pubs (Purdue University System) · 2018
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
FundersNatural Resources CanadaOffice of Research and Development
KeywordsMeteorologyWeather forecastingEnvironmental scienceComputer scienceClimatologyGeographyGeology
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.697
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.202
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it