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Wind power forecasting using artificial neural networks with numerical prediction : a case study for mountainous Canada

University of British Columbia

Wind is a free and easily available source of energy. Several countries, including Canada, have already incorporated wind power into their electricity supply system. Forecasting wind power production is quite challenging because the wind is variable and depends on weather conditions, terrain factors and turbine height. In addition to traditional physical and statistical methods, some advanced methods based on artificial intelligence have been investigated in recent years to achieve more reliable wind-power forecasts. The aim of this work is to exploit the ability of artificial neural network (ANN) models to find the most effective parameters to estimate generated power from predicted wind speed at a wind farm in mountainous Canada. The historical data of both observations and forecasts of weather characteristics along with turbine availabilities and the reported power production are used for this purpose. Experiments are done first with the observations (perfect-prog technique) to find the optimum architecture for the artificial neural network. Next to obtain a day-ahead forecast of the wind power, weather forecasts from a numerical weather prediction model was input to the optimum ANN as the predictors (model output statistics method). The results from ANN models are compared to linear-model fits in order to show the ability of ANN models to capture the nonlinearity effects. Also, another comparison is made between the results from artificial neural network models and the current approach used operationally by a utility company. The selected architecture is a three-layered feed-forward back-propagation ANN model with 8 hidden neurons. Verification results using an independent dataset show that the ANN method improves the day-ahead wind-power forecasts by up to 56% compared to the current operational approach.

Text

2016-08-25

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58978

Atmospheric Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/