Wind energy forecasting with missing values within a fully conditional specification framework
| Autor: | Honglin Wen, Pierre Pinson, Jie Gu, Zhijian Jin |
|---|---|
| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
0104 Statistics 1403 Econometrics FOS: Electrical engineering electronic engineering information engineering Applications (stat.AP) Econometrics Systems and Control (eess.SY) Business and International Management Statistics - Applications Electrical Engineering and Systems Science - Systems and Control 1505 Marketing Physics::Atmospheric and Oceanic Physics |
| Zdroj: | Web of Science |
| ISSN: | 0169-2070 |
| DOI: | 10.1016/j.ijforecast.2022.12.006 |
| Popis: | Wind power forecasting is essential to power system operation and electricity markets. As abundant data became available thanks to the deployment of measurement infrastructures and the democratization of meteorological modelling, extensive data-driven approaches have been developed within both point and probabilistic forecasting frameworks. These models usually assume that the dataset at hand is complete and overlook missing value issues that often occur in practice. In contrast to that common approach, we rigorously consider here the wind power forecasting problem in the presence of missing values, by jointly accommodating imputation and forecasting tasks. Our approach allows inferring the joint distribution of input features and target variables at the model estimation stage based on incomplete observations only. We place emphasis on a fully conditional specification method owing to its desirable properties, e.g., being assumption-free when it comes to these joint distributions. Then, at the operational forecasting stage, with available features at hand, one can issue forecasts by implicitly imputing all missing entries. The approach is applicable to both point and probabilistic forecasting, while yielding competitive forecast quality within both simulation and real-world case studies. It confirms that by using a powerful universal imputation method like fully conditional specification, the proposed approach is superior to the common approach, especially in the context of probabilistic forecasting. Comment: revision to International Journal of Forecasting |
| Databáze: | OpenAIRE |
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