Investigation of Factors Determining Wind Power Prediction Accuracy

Author, Institution: Giedrius Gecevičius, Lithuanian Energy Institute

Science Area, Field of Science: Technological Sciences, Energetics and Power Engineering – 06T

Scientific supervisor: Dr. Mantas Marčiukaitis (Lithuanian Energy Institute, Laboratory for Renewable Energy and Energy Efficiency, Technological Sciences, Energetics and Power Engineering – 06T).

Dissertation Defence Board of Energetics and Power Engineering Science Field:
Prof. Dr. Habil. Juozas Augutis (Vytautas Magnus University, Technological Sciences, Energetics and Power Engineering – 06T) – Chairman;
Dr. Viktorija Grigaitienė (Lithuanian Energy Institute, Technological Sciences, Energetics and Power Engineering – 06T);
Assoc. Prof. Dr. Audrius Jonaitis (Kaunas University of Technology, Technological Sciences, Energetics and Power Engineering – 06T);
Dr. Jūratė Kriaučiūnienė (Lithuanian Energy Institute, Technological Sciences, Environmental Engineering – 04T); Assoc. Prof. Dr. Francesco SCORZA (University of Basilicata, Technological Sciences, Environmental Engineering – 04T).

The doctoral dissertation is available at the libraries of Kaunas University of Technology (K. Donelaičio str. 20, Kaunas) and Lithuanian Energy Institute (Breslaujos g. 3, Kaunas).

Annotation:
Wind energy is one of the fastest developing renewable energy sectors in the world and in Lithuania. However, such rapid development of wind turbines is typically a challenge for the grid operator because it causes issues of grid balance and requires reserves. The solutions of this kind problems are wind power prediction systems and improvement of their accuracy. Therefore, the dissertation investigated the influence of different factors and carried out analysis of statistical and physical methods on wind power prediction, accurately estimated the influence of surface roughness and relief on wind speed variations and wind power forecasting accuracy, besides the most accurate predictive functions of wind power plants were determined. The paper also presents identification of the predictive power correction coefficients, as well optimal algorithms for short-term power forecasting, the most suitable parametric methods for mathematical wind power curves description were carried out. The analysis of the physical power forecasting methods revealed influence of wind shear and turbulence on the accuracy of power prediction processes. Considering to evaluation methods of long-term wind energy resources, the most accurate Weibull’s distribution parameters functions were determined at different wind speeds are as well. Considering to analysed factors and methods determining wind power prediction accuracy, new hybrid wind power forecasting method was developed, and it predicts wind turbine power by up to 4.7% more accurately, comparing to direct wind speed conversion from numerical weather prediction data.