Monthly Hydropower Generation Dataset for Western Canada
Bibliographic record
Abstract
The presented dataset contains the following simulation-based monthly hydropower generation data for 110 facilities in British Columbia and Alberta, to support Western-US interconnect grid system studies:1) Monthly hydropower generation estimates2) Monthly hydropower flexibility metrics (minimum and maximum hourly generation and daily fluctuations) The hydropower generation estimates are provided with reference to the facility list that contains the corresponding metadata for each facility. For more details, please refer to Son et al. (2024). Monthly hydropower generation data for Western Canada to support Western-US interconnect power system studies [Manuscript submitted for publication]. Corresponding author(s): Youngjun Son (youngjun.son@pnnl.gov) and Nathalie Voisin (nathalie.voisin@pnnl.gov) For data reproduction, please see the GitHub repository at https://github.com/GODEEEP/tgw-hydro-canada. Hydropower Facility List The file, CAN_hydropower_facilities.csv, provides essential information on 146 hydropower facilities in British Columbia and Alberta, derived from Renewable Energy Power Plants, 1 MW or more, by Energy Source by North American Cooperation on Energy Information (NACEI). Additionally, the facility information has been updated with corresponding National Hydrographic Network (NHN) Work Units, global reservoir and lake database (GRanD: Global Reservoirs and Dams Database and HydroLAKES), diversion intake flow rates based on water license information (hydropower), and so on. Below are the descriptions for each column in the facility metadata: fid: Facility id according to NACEI data. New four-digit id starting with '9' are assigned for facilities with no fid in NACEI data Facility: Name of the facility X: Longitude of the facility's powerhouse Y: Latitude of the facility's powerhouse Province: Province where the facility is located Hydro_MW: Nameplate capacity of the facility NHN_Work_U: Associated NHN Work Units GRanD_ID: Associated reservoir id from the GRanD dataset HydroLAKES_ID: Associated lake id from the HydroLAKES dataset GINDEX: Grid id from the mosartwmpy Canada model GINDEX_CONUS: Grid id from the mosartwmpy CONUS model, used for facilities in the Columbia River Basin Basin_Note: Indicator for facilities located in the Columbia River Basin or outside of the TGW meteorological forcing domain WECC_ADS_2032: Indicator for facilities without the WECC ADS 2032 reference hydropower generation data Intake_Flow_Rate: Diversion intake flow rates based on hydropower water license information Type: Type of facility Water_License: Link to the source of water license information Among the 146 hydropower facilities listed, only 110 facilities, which are within the TGW meteorological forcings domain and have reference hydropower generation data, are considered for monthly hydropower generation estimates. Monthly Hydropower Generation Estimates and Flexibility Metrics Each file contains a monthly timeseries dataset (rows: monthly timestamps) from 1981 to 2019 for 110 facilities (columns: Facility listed in CAN_hydropower_facilities.csv). CAN_hydropower_monthly_generation_MWh.csv: monthly total hydropower generation in MWh CAN_hydropower_monthly_p_min_MW.csv: monthly flexibility metric of minimum generation capacity in MW CAN_hydropower_monthly_p_max_MW.csv: monthly flexibility metric of maximum generation capacity in MW CAN_hydropower_monthly_p_ador_MW.csv: monthly flexibility metric of the daily operation range in MW Funding Acknowledgements This work was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at the Pacific Northwest National Laboratory (PNNL). The PNNL is a multi-program national laboratory operated by Battelle Memorial Institute for the U.S. Department of Energy (DOE) under Contract No. DE-AC05-76RL01830. Disclaimer The presented dataset was prepared as an account of work sponsored by an agency of the U.S. Government. Neither the U.S. Government nor the U.S. Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof, or Battelle Memorial Institute.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.021 |
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 source (direct Gemma or distilled Codex), 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".