IEA EBC Annex 80 "Typical and extreme weather datasets for studying the resilience of buildings to climate change" (Version 1.0)
Bibliographic record
Abstract
Project: IEA EBC Annex 80 Resilient Cooling for Buildings - Weather data - The Annex 80 (https://annex80.iea-ebc.org/) is a project by the International Energy Agency - Energy in Buildings and Communities programme(https://www.iea-ebc.org/). The Annex 80’s main objective is to support a rapid transition to an environment where resilient low energy and low carbon cooling systems are the mainstream and preferred solutions for cooling and overheating issues in buildings. Resilient Cooling is used to denote low energy and low carbon cooling solutions that strengthen the ability of individuals and our community as a whole to withstand, and also prevent, thermal and other impacts of changes in global and local climates. Summary: This experiment collects the datasets created by the "Weather Data" group of the IEA EBC Annex 80 “Resilient Cooling for Buildings” project. These are datasets of current and future weather files for building energy performance simulation covering 15 locations in ten climate zones worldwide. The datasets contain ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets include typical and extreme weather years in the EnergyPlus weather file (EPW) format and multi-year projections in comma-separated value (CSV) format for three periods: historical (2001-2020), future mid-term (2041-2060), and future long-term (2081-2100). The weather files were generated based on the climate projections from the Regional Climate Model (RCM) MPI-RCA4, then bias-corrected using multiyear observational data for each city. The weather files are ready to be used in building energy simulations and systems design for adaptation and resilience studies. The EPW is a weather file format used to run simulations in EnergyPlus. EPWs are text files and can be opened and edited in any text editor, spreadsheet tools or open-source software tools for creating and editing customised weather files, such as the Element software developed by Big Ladder Software (https://bigladdersoftware.com/projects/elements/). The EnergyPlus Auxiliary Programmes document (attached Additional Info) describes EPW weather data and provides general information on weather data for energy simulations and weather file conversion. Funder: - The Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Office, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 - The Horizon 2020 project 0E-BUILDINGS, Grant agreement ID: 101024627 - The Marie Skłodowska-Curie grant agreement Nº 101024627 - The Fraunhofer Internal Programs under Grant No. Attract 003-695033 - Det Energiteknologisk Udviklingsog Demonstrations Program (EUDP) under grant 64018-0578
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".