The inherent randomness,intermittence and volatility of wind power generation compromise the quality of the wind power system,resulting in uncertainty in the system’s optimal scheduling.As a result,it’s critical to ...The inherent randomness,intermittence and volatility of wind power generation compromise the quality of the wind power system,resulting in uncertainty in the system’s optimal scheduling.As a result,it’s critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation.Inferred statistics are utilized in this research to infer general features based on the selected information,confirming that there are differences between two forecasting categories:Forecast Category 1(0-11 h ahead)and Forecast Category 2(12-23 h ahead).In z-tests,the null hypothesis provides the corresponding quantitative findings.To verify the final performance of the prediction findings,five benchmark methodologies are used:Persistence model,LMNN(Multilayer Perceptron with LMlearningmethods),NARX(Nonlinear autoregressive exogenous neural networkmodel),LMRNN(RNNs with LM training methods)and LSTM(Long short-term memory neural network).Experiments using a real dataset show that the LSTM network has the highest forecasting accuracy when compared to other benchmark approaches including persistence model,LMNN,NARX network,and LMRNN,and the 23-steps forecasting accuracy has improved by 19.61%.展开更多
These days when I look at scientific research papers or review manuscripts,there seems to be almost a competition to have a smaller p value as a means to present more significant findings.For example,a quick Internet ...These days when I look at scientific research papers or review manuscripts,there seems to be almost a competition to have a smaller p value as a means to present more significant findings.For example,a quick Internet search using＂p〈0.0000001＂turned up many papers even reporting their p values at this level.Can and should a smaller p value play such a role？In my opinion,it cannot.展开更多
基金This research is supported by National Natural Science Foundation of China(No.61902158).
文摘The inherent randomness,intermittence and volatility of wind power generation compromise the quality of the wind power system,resulting in uncertainty in the system’s optimal scheduling.As a result,it’s critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation.Inferred statistics are utilized in this research to infer general features based on the selected information,confirming that there are differences between two forecasting categories:Forecast Category 1(0-11 h ahead)and Forecast Category 2(12-23 h ahead).In z-tests,the null hypothesis provides the corresponding quantitative findings.To verify the final performance of the prediction findings,five benchmark methodologies are used:Persistence model,LMNN(Multilayer Perceptron with LMlearningmethods),NARX(Nonlinear autoregressive exogenous neural networkmodel),LMRNN(RNNs with LM training methods)and LSTM(Long short-term memory neural network).Experiments using a real dataset show that the LSTM network has the highest forecasting accuracy when compared to other benchmark approaches including persistence model,LMNN,NARX network,and LMRNN,and the 23-steps forecasting accuracy has improved by 19.61%.
文摘These days when I look at scientific research papers or review manuscripts,there seems to be almost a competition to have a smaller p value as a means to present more significant findings.For example,a quick Internet search using＂p〈0.0000001＂turned up many papers even reporting their p values at this level.Can and should a smaller p value play such a role？In my opinion,it cannot.
