Wind Energy Research and Forecast

Exhibitor: Dan Luo

Supervisor: Zhao Dong

Research Group: Complex and Intelligent Systems

Industry Sector: Energy and Utilities

Wind energy is a kind of clean and renewable source of electric power. It is also the world's fastest growing energy source. It’s development gives great benefit for human being’s health, generation and Australia National Electricity Market (NEM). It is one of the cheapest and cleanest renewable energy resources available. It has protection against electricity price stability purchasing electricity generated by renewable energy. In this thesis, the wind energy is analysed by doing forecast on hourly wind data for last few months. The historical wind speed data are gathered from some windy areas, such as, Brisbane region, Gold Coast region, Macay region and some other region from other states in Australia. All the data are obtained from the Bureau of Meteorology QLD and VIC. The auto-regressive moving average (ARMA) forecast model is adapted based on the historical wind speed data, which provides wind energy industry and all relevant industries with an expectation of their future wind energy development. It evaluates the data to predict a future pattern of wind speed trend. The wind speed data is also treated as a time-series signal and is decomposed into a number of wavelet coefficients. In that case the wind speed data can be analysed in more details, and shows more clear characteristics. Finally the ARMA model will apply to the decomposed coefficients from wavelet analysis to predict more accurate pattern to produce the final forecast. It helps designing filters or motors on wind energy technology.

Thesis Document (PDF)