Datasets:

neurips26-PSML
/

SIIB-Time

@@ -61,7 +61,7 @@ Two distinct field epistemologies are in tension in the design of this dataset.
 Each simulation run requires approximately 7 minutes of CPU execution time. Assuming an average computational power draw of 100 W for the host system [19], the energy consumption per simulation run is 7 min × (1 hr / 60 min) × 0.1 kW = 0.0117 kWh per simulation. The data was generated in Alberta; thus, using the Alberta grid emission intensity of 0.47 kg CO₂e/kWh for electricity generation [20], the carbon footprint per simulation run is approximately 0.0117 kWh × 0.47 kg CO₂e/kWh ≈ 5.5 g CO₂e per simulation run. Across all 4,000 simulation runs, the total estimated carbon footprint is 4,000 × 5.5 g ≈ 22 kg CO₂e. For reference, this is roughly equivalent to driving a passenger vehicle approximately 107 kilometers [21], making the dataset generation process environmentally modest relative to the potential research impact of enabling more computationally efficient grid simulation tools.
-The dataset creation involves a trade-off between simulation fidelity and computational and environmental cost. EMT simulations were necessary to accurately capture fast inverter dynamics, provide reliable ground truth for machine learning models, and enable cross-resolution learning between EMT and RMS domains. At the same time, the dataset size of 4000 scenarios was selected to provide sufficient diversity for machine learning applications, while keeping the overall computational footprint within a manageable range.
 # 3- Data Pipeline
 All data were produced by running 4,000 distinct operational scenarios across four simulation configurations and GFM and GFL control modes, each simulated in both the EMT (PSCAD) and RMS phasor domains (MATLAB/Simulink), yielding matched trajectory pairs for each scenario. Each simulation starts from the system being in steady-state and runs for 6.5 simulated seconds.

 Each simulation run requires approximately 7 minutes of CPU execution time. Assuming an average computational power draw of 100 W for the host system [19], the energy consumption per simulation run is 7 min × (1 hr / 60 min) × 0.1 kW = 0.0117 kWh per simulation. The data was generated in Alberta; thus, using the Alberta grid emission intensity of 0.47 kg CO₂e/kWh for electricity generation [20], the carbon footprint per simulation run is approximately 0.0117 kWh × 0.47 kg CO₂e/kWh ≈ 5.5 g CO₂e per simulation run. Across all 4,000 simulation runs, the total estimated carbon footprint is 4,000 × 5.5 g ≈ 22 kg CO₂e. For reference, this is roughly equivalent to driving a passenger vehicle approximately 107 kilometers [21], making the dataset generation process environmentally modest relative to the potential research impact of enabling more computationally efficient grid simulation tools.
+The dataset creation involves a trade-off between simulation fidelity and computational and environmental cost. EMT simulations were necessary to accurately capture fast inverter dynamics, provide reliable ground truth for machine learning models, and enable cross-resolution learning between EMT and RMS domains. At the same time, the dataset size of 3000 scenarios was selected to provide sufficient diversity for machine learning applications, while keeping the overall computational footprint within a manageable range.
 # 3- Data Pipeline
 All data were produced by running 4,000 distinct operational scenarios across four simulation configurations and GFM and GFL control modes, each simulated in both the EMT (PSCAD) and RMS phasor domains (MATLAB/Simulink), yielding matched trajectory pairs for each scenario. Each simulation starts from the system being in steady-state and runs for 6.5 simulated seconds.