Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6763584 | Renewable Energy | 2019 | 18 Pages |
Abstract
The authors developed a forecasting model for Luxembourg, able to predict the expected regional PV power up to 72â¯h ahead. The model works with solar irradiance forecasts, based on numerical weather predictions in hourly resolution. Using a set of physical equations, the algorithm is able to predict the expected hourly power production for PV systems in Luxembourg, as well as for a set of 23 chosen PV-systems which are used as reference systems. Comparing the calculated forecasts for the 23 reference systems to their measured power over a period of 2 years, revealed a comparably high accuracy of the forecast. The mean deviation (bias) of the forecast was 1.1% of the nominal power - a relatively low bias indicating low systemic error. The root mean square error (RMSE), lies around 7.4% - a low value for single site forecasts. Two approaches were tested in order to adapt the short-term forecast, based on the present forecast deviations for the reference systems. Thereby, it was possible to improve the very short term forecast on the time horizon of 1-3â¯h ahead, specifically for the remaining bias, but also systemic deviations can be identified and partially corrected (e.g. snow cover).
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
Daniel Koster, Frank Minette, Christian Braun, Oliver O'Nagy,