0000000001083598
AUTHOR
Pål Preede Revheim
Identification and Handling of Critical Irradiance Forecast Errors Using a Random Forest Scheme – A Case Study for Southern Brazil
Abstract Large forecast errors of solar power prediction cause challenges for the management of electric grids. Here, the classification technique Random Forests is applied to analyze the possible linkage of hourly or daily forecast errors to the actual situation given by a set of meteorological variables. This form a prediction of the forecast error and is thus usable to update the forecast. The performance of this scheme is assessed for the example of irradiance forecasts in Brazil. While limited to none improvements are obtained for next-hour forecasts, significant improvements are obtained for the next-day forecasts.
Improving and enhancing NWP based wind power forecasts under Norwegian conditions
Doktorgradsavhandling i ingeniørvitenskap, Universitetet i Agder, 2015 This thesis studies methods for improving and enhancing NWP based wind power forecasts for cases from the Western coast of Norway. This is an area with excellent wind conditions, but where the installed wind power capacity at present is limited. The area is characterized by a rugged coastline and complex terrain, which have earlier been shown to lead to high wind power forecast errors. The overall aim of the thesis is to study how this kind of conditions influence wind power forecast errors and to investigate how wind power forecast models can be made more resistant to the challenges these conditions pose. The data basis…