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

This repository contains the code and data for the following paper:

Sahand Karimi-Arpanahi, S. Ali Pourmousavi, and Nariman Mahdavi. "Quantifying the predictability of renewable energy data for improving power systems decision making."

Abstract:

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 predictability of time series data cannot be measured by existing prediction techniques. This important measure has been overlooked by researchers and practitioners in the power systems domain. In this paper, we systematically assess the suitability of various predictability measures for renewable generation time series data, revealing the best method and providing instructions for tuning it. Then, using real-world examples, we illustrate how predictability could save end users and investors millions of dollars in the electricity sector.

Please refer to the relevant Github repository for further details.