Error-Level-Controlled Synthetic Forecasts for Renewable Generation
Renewable energy resources, including solar and wind energy, play a significant role in sustainable energy systems. However, the inherent uncertainty and intermittency of renewable generation pose challenges to the safe and efficient operation of power systems. Recognizing the importance of short-term (hours ahead) renewable generation forecasting in power systems operation, it becomes crucial to address the potential inaccuracies in these forecasts. To systematically evaluate the performance of controllers in the presence of imperfect forecasts, we generate synthetic forecasts using actual renewable generation profiles (one from solar and one from wind). These synthetic forecasts incorporate different levels of statistical error, allowing us to control and manipulate the accuracy of the predictions. The primary objective is to employ synthetic forecasts with controlled yet realistic error levels to systematically investigate how controllers adapt to variations in forecast accuracy, providing valuable insights into their robustness and effectiveness under real-world conditions.
Citation Formats
TY - DATA
AB - Renewable energy resources, including solar and wind energy, play a significant role in sustainable energy systems. However, the inherent uncertainty and intermittency of renewable generation pose challenges to the safe and efficient operation of power systems. Recognizing the importance of short-term (hours ahead) renewable generation forecasting in power systems operation, it becomes crucial to address the potential inaccuracies in these forecasts. To systematically evaluate the performance of controllers in the presence of imperfect forecasts, we generate synthetic forecasts using actual renewable generation profiles (one from solar and one from wind). These synthetic forecasts incorporate different levels of statistical error, allowing us to control and manipulate the accuracy of the predictions. The primary objective is to employ synthetic forecasts with controlled yet realistic error levels to systematically investigate how controllers adapt to variations in forecast accuracy, providing valuable insights into their robustness and effectiveness under real-world conditions.
AU - Zhang, Xiangyu
A2 - Knueven, Bernard
A3 - Eseye, Abinet Tesfaye
A4 - Reynolds, Matthew
A5 - Jones, Wesley
A6 - Liu, Weijia
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO - 10.25984/2222585
KW - energy
KW - power
KW - renewable forecasts
KW - forecast error
KW - stochastic optimization
KW - optimal control
KW - uncertainty
KW - forecast
KW - forecasting
KW - power systems operation
KW - renewable generation
KW - synthetic forecast
KW - energy systems integration
KW - wind power
KW - solar power
KW - grid
KW - renewable uncertainty
KW - controllers
KW - short-term generation
LA - English
DA - 2021/06/01
PY - 2021
PB - National Renewable Energy Laboratory (NREL)
T1 - Error-Level-Controlled Synthetic Forecasts for Renewable Generation
UR - https://doi.org/10.25984/2222585
ER -
Zhang, Xiangyu, et al. Error-Level-Controlled Synthetic Forecasts for Renewable Generation. National Renewable Energy Laboratory (NREL), 1 June, 2021, Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2222585.
Zhang, X., Knueven, B., Eseye, A., Reynolds, M., Jones, W., & Liu, W. (2021). Error-Level-Controlled Synthetic Forecasts for Renewable Generation. [Data set]. Open Energy Data Initiative (OEDI). National Renewable Energy Laboratory (NREL). https://doi.org/10.25984/2222585
Zhang, Xiangyu, Bernard Knueven, Abinet Tesfaye Eseye, Matthew Reynolds, Wesley Jones, and Weijia Liu. Error-Level-Controlled Synthetic Forecasts for Renewable Generation. National Renewable Energy Laboratory (NREL), June, 1, 2021. Distributed by Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2222585
@misc{OEDI_Dataset_5978,
title = {Error-Level-Controlled Synthetic Forecasts for Renewable Generation},
author = {Zhang, Xiangyu and Knueven, Bernard and Eseye, Abinet Tesfaye and Reynolds, Matthew and Jones, Wesley and Liu, Weijia},
abstractNote = {Renewable energy resources, including solar and wind energy, play a significant role in sustainable energy systems. However, the inherent uncertainty and intermittency of renewable generation pose challenges to the safe and efficient operation of power systems. Recognizing the importance of short-term (hours ahead) renewable generation forecasting in power systems operation, it becomes crucial to address the potential inaccuracies in these forecasts. To systematically evaluate the performance of controllers in the presence of imperfect forecasts, we generate synthetic forecasts using actual renewable generation profiles (one from solar and one from wind). These synthetic forecasts incorporate different levels of statistical error, allowing us to control and manipulate the accuracy of the predictions. The primary objective is to employ synthetic forecasts with controlled yet realistic error levels to systematically investigate how controllers adapt to variations in forecast accuracy, providing valuable insights into their robustness and effectiveness under real-world conditions.},
url = {https://data.openei.org/submissions/5978},
year = {2021},
howpublished = {Open Energy Data Initiative (OEDI), National Renewable Energy Laboratory (NREL), https://doi.org/10.25984/2222585},
note = {Accessed: 2025-04-25},
doi = {10.25984/2222585}
}
https://dx.doi.org/10.25984/2222585
Details
Data from Jun 1, 2021
Last updated Nov 29, 2023
Submitted Nov 29, 2023
Organization
National Renewable Energy Laboratory (NREL)
Contact
Xiangyu Zhang
303.275.4068
Authors
Research Areas
Keywords
energy, power, renewable forecasts, forecast error, stochastic optimization, optimal control, uncertainty, forecast, forecasting, power systems operation, renewable generation, synthetic forecast, energy systems integration, wind power, solar power, grid, renewable uncertainty, controllers, short-term generationDOE Project Details
Project Name Improving Distribution System Resiliency via Deep Reinforcement Learning
Project Number 36292