Abstract: Many models have been developed to forecast wind farm power output. It is generally difficult to determine whether the performance of one model is consistently better than that of another model under all circumstances. Motivated by this finding, we aimed to integrate groups of models into an aggregated model using fuzzy theory to obtain further performance improvements. First, three groups of least squares support vector machine (LS-SVM) forecasting models were developed: univariate LS-SVM models, hybrid models using auto-regressive moving average (ARIMA) and LS-SVM and multivariate LS-SVM models. Each group of models is selected by a decorrelation maximisation method, and the remaining models can be regarded as experts in forecasting. Next, fuzzy aggregation and a defuzzification procedure are used to combine all of these forecasting results into the final forecast. For sample randomization, we statistically compare models. Results show that this group-forecasting model performs well in terms of accuracy and consistency.
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Zhang, Q.; Lai, K.K.; Niu, D.; Wang, Q.; Zhang, X. A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power. Energies 2012, 5, 3329-3346.
Zhang Q, Lai KK, Niu D, Wang Q, Zhang X. A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power. Energies. 2012; 5(9):3329-3346.
Zhang, Qian; Lai, Kin Keung; Niu, Dongxiao; Wang, Qiang; Zhang, Xuebin. 2012. "A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power." Energies 5, no. 9: 3329-3346.