In English with abstract in English and German

Originally presented as the author's thesis (doctoral)--Universitèat Kassel, 2017.

Online resource; title from PDF title page (EBSCO, viewed April 17, 2018).

Includes bibliographical references.

""Front Cover""; ""Title Page""; ""Imprint""; ""Abstract (English)""; ""Abstract (German)""; ""Content""; ""List of figures""; ""List of tables""; ""Abbreviations""; ""Acknowledgement""; ""1 Introduction and summary""; ""1.1 Development of offshore wind energy""; ""1.1.1 Offshore wind power in Europe""; ""1.1.2 Offshore wind power world wide""; ""1.1.3 Challenges of offshore wind energy""; ""1.2 Motivation, objectives, problem statement and focus""; ""1.2.1 Motivation""; ""1.2.2 Problem statement""; ""1.2.3 Purpose of the PhD work""; ""1.2.4 Publications""

""1.2.5 Outline of the thesis and exhaustive summary""""2 State of the art in wind power forecasting""; ""2.1 Application of wind power forecasting in energy trade in Germany""; ""2.1.1 Persistence wind power forecasting""; ""2.1.2 Short term wind power forecasting""; ""2.1.3 Day-ahead wind power forecasting""; ""2.2 Energy meteorology and numerical weather predictions""; ""2.3 State of wind power forecasting methods and research""; ""2.3.1 State of offshore wind power forecasting""; ""2.3.2 Existing wind power forecasting methods""; ""2.4 Existing applications for wind power forecasting""

""2.5 Summary""""3 Input data for development of forecasting models""; ""3.1 Available data""; ""3.2 Development of a plausibility check for meteorological parameters""; ""3.2.1 Correction of measurements from FINO1 meteorological mast""; ""3.2.2 Determination of wind sectors disturbed by the wind farm""; ""3.2.3 Validation of FINO1 wind speed measurements""; ""3.3 Plausibility of power data and detection of installed wind power""; ""3.4 Assessment of the accuracy of wind power forecasting""; ""3.5 Summary""; ""4 Development and implementation of models for wind power forecasting""

""4.1 Development of a physical model based on power curve""""4.1.1 WAPPM -- Wake Adjusted Physical Power Model""; ""4.1.2 Optimization of WAPPM with Model Output Statistics (MOS)""; ""4.1.3 Simulation of the wind power time series of alpha ventus""; ""4.2 Wind power forecasting using WAPPM""; ""4.2.1 Physical model without considering wake effects""; ""4.2.2 Physical model with consideration of wake effects""; ""4.2.3 Adapted physical model""; ""4.2.4 Physical model extended with model output statistics""; ""4.3 Artificial neural networks in wind power forecasting""

""4.3.1 Application of artificial neural networks""""4.3.2 Variation of prediction error dependent on hidden neurons""; ""4.4 Development of ensemble wind power forecast models""; ""4.4.1 Ensemble physical wind power forecasting""; ""4.4.2 Simple averaging of the predictions of different forecasting methods""; ""4.4.3 Hybrid system 1 ãa#x80;#x93; WAPPM prediction as additional input to ANN""; ""4.4.4 Hybrid system 2 ãa#x80;#x93; Double ANN 1""; ""4.4.5 Hybrid system 3 ãa#x80;#x93; Double ANN 2""; ""4.5 Summary""; ""5 Consecutive selection of learning approach and physical model""

Master record variable field(s) change: 072, 650