Quantifying the predictability of renewable energy data for improving power systems decision-making

• Published in: Patterns (New York, N.Y.) Vol. 4; no. 4; p. 100708
• Main Authors: Karimi-Arpanahi, Sahand, Pourmousavi, S. Ali, Mahdavi, Nariman
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 14.04.2023; Elsevier
• Subjects: electricity market analysis; generation predictability; power systems data analysis; power systems decision making; PV generation time series; renewable generation forecasting; time series predictability; weighted permutation entropy; time series predictability; weighted permutation entropy; renewable generation forecasting; PV generation time series; electricity market analysis; DSML 3: Development/pre-production: Data science output has been rolled out/validated across multiple domains/problems; generation predictability; power systems data analysis; power systems decision making

Decision-making in the power systems domain often relies on predictions of renewable generation. While sophisticated forecasting methods have been developed to improve the accuracy of such predictions, their accuracy is limited by the inherent predictability of the data used. However, the predictabi...