GA-ANN for Short-Term Wind Energy Prediction

Wind turbine power output is totally intermittent in the nature. For grid connected wind turbine generators, power system operators (transmission system operators) need reliable and robust wind power forecasting system. Rapid changes in the wind generation relative to the load require proper energy management system to maintain the power system stability and of course to balance the power generation, frequency, voltage regulation within the statutory limits. Accurate wind energy forecasting helps the power system transmission system operators in anticipating rapid changes in wind turbine power output with respect to load and helps in making decision not only for optimum energy management but also for energy trading in the open electricity market. In this article, wind energy prediction for short term has been done by using artificial network in combination with genetic algorithms. The developed model has tested and analyzed for Taiwan Wind Power company's real operational results. The results show that the combination of ANN and GA model gives wind power output prediction very well except during the occurrences of gust. It has been observed that ANN performs well in non-linear mapping, but the combination of ANN-GA gives more accurate prediction. This model has been implemented in different time scales, which will also be useful for wind energy trading in the open electricity market.