Abstract
Weather variables are an important driver of power generation from renewable energy sources. However, accurately predicting such variables is a challenging task, which has a significant impact on the accuracy of the power generation forecasts. In this study, we explore the impact of imperfect weather forecasts on two classes of forecasting methods (statistical and machine learning) for the case of wind power generation. We perform a stress test analysis to measure the robustness of different methods on the imperfect weather input, focusing on both the point forecasts and the 95% prediction intervals. The results indicate that different methods should be considered according to the uncertainty characterizing the weather forecasts.
| Original language | English |
|---|---|
| Article number | 1880 |
| Pages (from-to) | 1-18 |
| Number of pages | 18 |
| Journal | Energies |
| Volume | 13 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 12 Apr 2020 |
Bibliographical note
Funding Information:We are grateful to Nikos Hatziargyriou and George Sideratos of the National Technical University of Athens, as well as the EU SafeWind Project for providing the data.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Keywords
- Forecasting
- Machine learning
- Uncertainty
- Weather forecasts
- Wind power
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- Energy Engineering and Power Technology
- Energy (miscellaneous)
- Control and Optimization
- Electrical and Electronic Engineering
