Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting
Open Access
- 1 January 2014
- journal article
- research article
- Published by Hindawi Limited in Advances in Mathematical Physics
- Vol. 2014, 1-11
Abstract
Large scale integration of wind generation capacity into power systems introduces operational challenges due to wind power uncertainty and variability. Therefore, accurate wind power forecast is important for reliable and economic operation of the power systems. Complexities and nonlinearities exhibited by wind power time series necessitate use of elaborative and sophisticated approaches for wind power forecasting. In this paper, a local neurofuzzy (LNF) approach, trained by the polynomial model tree (POLYMOT) learning algorithm, is proposed for short-term wind power forecasting. The LNF approach is constructed based on the contribution of local polynomial models which can efficiently model wind power generation. Data from Sotavento wind farm in Spain was used to validate the proposed LNF approach. Comparison between performance of the proposed approach and several recently published approaches illustrates capability of the LNF model for accurate wind power forecasting.Keywords
This publication has 12 references indexed in Scilit:
- Current methods and advances in forecasting of wind power generationRenewable Energy, 2012
- Short-term wind power forecasting using ridgelet neural networkElectric Power Systems Research, 2011
- Hybrid intelligent approach for short-term wind power forecasting in PortugalIET Renewable Power Generation, 2011
- Wind Power Prediction by a New Forecast Engine Composed of Modified Hybrid Neural Network and Enhanced Particle Swarm OptimizationIEEE Transactions on Sustainable Energy, 2011
- Bayesian adaptive combination of short-term wind speed forecasts from neural network modelsRenewable Energy, 2011
- Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN modelRenewable Energy, 2010
- Wind Power Density Forecasting Using Ensemble Predictions and Time Series ModelsIEEE Transactions on Energy Conversion, 2009
- Day-ahead wind speed forecasting using f-ARIMA modelsRenewable Energy, 2009
- Improvements in wind speed forecasts for wind power prediction purposes using Kalman filteringJournal of Wind Engineering and Industrial Aerodynamics, 2008
- Nonlinear System Identification2001