Short-term wind power forecasting and uncertainty analysis based on FCM–WOA–ELM–GMM

With large-scale wind power connected to the power grid, accurate short-term wind power forecasting has become a key technology for safe, economic power grid operation. Therefore, a short-term wind power forecasting and uncertainty analysis method based on the FCM-WOA-ELM-GMM was proposed. Fuzzy C-means (FCM) was used to cluster the numerical weather prediction (NWP) and wind farm power data, and the data points with similar meteorological information are classified into one class. Using the rapid convergence and high convergence accuracy of the whale optimization algorithm (WOA), the input weight and hidden layer threshold of the extreme learning machine (ELM) model were optimized to improve the ELM calculation speed and forecasting accuracy. The WOA-ELM model was trained using the clustered NWP and wind farm power data and the short-term wind power was projected using the trained forecasting model. To accurately calculate the forecasting error probability density distribution, the Gaussian mixture model (GMM) was applied and the wind power forecast confidence intervals under different climatic conditions and time scales were calculated. The forecasting accuracies of the WOA-ELM, ELM, PSO-LSSVM, LSSVM, LSTM, PSO-BP, and WNN models were compared and analyzed, and the RMSE values of the 4-h forecasting results in April were as follows: WOA-ELM, 5.95%; ELM, 26.73%; PSO-LSSVM, 3.78%; LSSVM, 5.19%; LSTM, 23.71%; PSO-BP, 15.63%; and WNN, 23.23%. The RMSE values of 24-h forecasting results in April were: WOA-ELM, 6.62%; ELM, 19.86%; PSO-LSSVM, 9.91%; LSSVM, 13.73%; LSTM, 23.69%; PSO-BP, 14.08%; and WNN, 20.11%. The RMSE values of 72-h forecasting results in April were as follows: WOA-ELM, 5.24%; ELM, 13.64%; PSO-LSSVM, 12.03%; LSSVM, 13.67%; LSTM, 16.61%; PSO-BP, 15.46%; and WNN, 20.22%. According to the calculation results, under different climatic conditions and forecasting time scales, the forecasting accuracy of the FCM-WOA-ELM-GMM model is higher than those of the other models.