Electricity Energy Consumption in the Gran Buenos Aires (metropolitan area) from 2012 to 2018 --- CAMMESA data
Bibliographic record
Abstract
The Electricity Energy Consumption in the Gran Buenos Aires (EECGBA) dataset contains nine variables reported hourly from the beginning of January 1st, 2012, until 11 pm on December 31st, 2018 (61,368 rows). The data was collected by the Compañía Administradora del Mercado Mayorista Eléctrico Sociedad Anónima (CAMMESA). The data was generously offered to researchers at Universidad Nacional del Sur (UNS) (Argentina) to be used in research projects involving UNS and Dalhousie University (Canada). The dataset describes weather data (temperature, humidity, and so on) and electricity consumption (in MWh) for the Gran Buenos Aires (GBA) (Buenos Aires city metropolitan area) geographical area. Additionally, we also included in the dataset two variables related to the economic activity status of that day. First, we included the Argentinian monthly estimator of economic activity (EMAE in Spanish) produced by the INDEC (National Institute of Statistics and Census of Argentina). Second, we also included a boolean variable that indicates if that day was a holiday in Argentina (which means a large part of the population didn't work that day). In summary, the dataset includes ten columns. The first column is the date and time of each data entry (format YYYY-MM-DD HH:MM:SS), and the following nine columns describe the following information: - The first variable, DemGBA, describes the electrical consumption of the GBA measured in megawatt hours (MWh). - The second variable, Temp, measures the hourly ambient temperature in GBA in degrees Celsius. - The third variable, Holiday, is a boolean variable that indicates if on that day there was a holiday in Argentina (meaning it was a non-workable day). - The fourth variable, EMAE, is the Argentinian monthly estimator of economic activity produced by the INDEC. - The fifth variable, vy, is the south-north wind component in km/h. - The sixth variable, vx, is the west-est wind component in km/h. - The seventh variable, GHI, is the Global Horizontal Irradiance (the total solar radiation incident on a horizontal surface) measured in watts per square meter (W/m2). - The eighth variable, Hum, describes the ambient relative humidity measured as a percentage. - The ninth variable, Pres, measures the atmospheric pressure in hectopascals (hPa). All variables present an hourly frequency (updated every hour) except "Holiday," which changes daily, and the EMAE, which changes monthly. How to read the data in python: import pandas as pd df = pd.read_csv('cammesa_db_2012_2018.csv',index_col=0) Acknowledgments: We greatly thank the Compañía Administradora del Mercado Mayorista Eléctrico Sociedad Anónima (CAMMESA) for providing the data.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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".