UID:
kobvindex_HPB1007700766
Format:
1 online resource :
,
color illustrations
ISBN:
9783737603478
,
3737603472
Note:
Originally presented as the author's thesis (doctoral)--Universität Kassel, 2017.
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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
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1.2.5 Outline of the thesis and exhaustive summary2 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
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2.5 Summary3 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
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4.1 Development of a physical model based on power curve4.1.1 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
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4.3.1 Application of artificial neural networks4.3.2 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 â#x80;#x93; WAPPM prediction as additional input to ANN -- 4.4.4 Hybrid system 2 â#x80;#x93; Double ANN 1 -- 4.4.5 Hybrid system 3 â#x80;#x93; Double ANN 2 -- 4.5 Summary -- 5 Consecutive selection of learning approach and physical model
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In English with abstract in English and German.
Additional Edition:
Print version: Kurt, Melih. Development of an offshore specific wind power forecasting system. Kassel, [Germany] : kassel university press GmbH, ©2017 ISBN 9783737603461
Language:
English
