Short term wind power forecasting using hybrid models

This study improves the Short term wind power forecasting to help bid the wind power in the electricity market. Supplying power lesser/greater than the expected power creates imbalance in the Electricity system. Hence electricity markets impose penalty for supplying power lesser/greater than expected power. Bidding right amount of power is an important issue for the electricity power producers. This issue is very relevant for a wind power producer due to the inherent nature of wind. Wind is characterised by uncertainty and volatility. This study proposes hybrid approaches that use the meteorological forecast of wind power and statistical models to improve the accuracy of the wind power forecast over meteorological forecast. The statistical methods used in the study are linear regression model, Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-Nearest Neighbour. The production data of ten wind farms in a portfolio, meteorological forecasts of the ten wind farms, total production of the portfolio and meteorological forecast of total production were collected for 532 days for every hour. These data were used to train and test the hybrid models. These hybrid models are then compared empirically with the meteorological forecasts. It is found that, for the data used for the study, hybrid model using artificial neural network performs the best but only slightly over the linear regression model. Followed by artificial neural network and linear regression model is support vector machine. Followed by support vector machine is K-Nearest Neighbour model. But all the hybrid models performed better than the meteorological forecast of wind power.