Selection of Numerical Weather Forecast Features for PV Power Predictions with Random Forests
| Autor: | Oliver Kramer, Björn Wolff, Detlev Heinemann |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Global Forecast System
Meteorology Cost efficiency business.industry Computer science 020209 energy 020208 electrical & electronic engineering Photovoltaic system 02 engineering and technology Numerical weather prediction Electrical grid Reliability engineering Renewable energy Random forest Nameplate capacity 0202 electrical engineering electronic engineering information engineering business Physics::Atmospheric and Oceanic Physics |
| Zdroj: | Data Analytics for Renewable Energy Integration ISBN: 9783319509464 DARE@PKDD/ECML |
| DOI: | 10.1007/978-3-319-50947-1_8 |
| Popis: | The increasing volatility introduced to power grids by renewable energy sources makes it necessary that the accuracy of energy forecasts are improved. Photovoltaic (PV) power plants hold the biggest share of installed capacity of renewable energy in Germany, so that high quality PV power forecasts are vital for a cost efficient operation of the underlying electrical grid. In this paper, we evaluate multiple Numerical Weather Prediction (NWP) parameters for their ability to improve PV power forecasting features. The importance of features is decided by a Random Forest algorithm. Furthermore, the resulting top ranked features are tested by performing PV power forecasts with Support Vector Regression, Random Forest, and linear regression models. |
| Databáze: | OpenAIRE |
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