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Nonparametric Probabilistic Prediction of Regional PV Outputs Based on Granule-based Clustering and Direct Optimization Programming 被引量:3
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作者 Yonghui Sun Yan Zhou +5 位作者 Sen Wang Rabea Jamil Mahfoud Hassan Haes Alhelou George Sideratos Nikos Hatziargyriou Pierluigi Siano 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2023年第5期1450-1461,共12页
Regional photovoltaic(PV) power prediction plays an important role in power system planning and operation. To effectively improve the performance of prediction intervals(PIs) for very short-term regional PV outputs, a... Regional photovoltaic(PV) power prediction plays an important role in power system planning and operation. To effectively improve the performance of prediction intervals(PIs) for very short-term regional PV outputs, an efficient nonparametric probabilistic prediction method based on granulebased clustering(GC) and direct optimization programming(DOP) is proposed. First, GC is proposed to formulate and cluster the sample granules consisting of numerical weather prediction(NWP) and historical regional output data, for the enhanced hierarchical clustering performance. Then, to improve the accuracy of samples' utilization, an unbalanced extension is used to reconstruct the training samples consisting of power time series. After that, DOP is applied to quantify the output weights based on the optimal overall performance. Meanwhile, a balance coefficient is studied for the enhanced reliability of PIs. Finally, the proposed method is validated through multistep PIs based on the numerical comparison of real PV generation data. 展开更多
关键词 Regional photovoltaic output prediction intervals granule-based clustering direct optimization programming nonparametric probabilistic prediction
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Imprecise Probability Method with the Power-Normal Model for Accelerated Life Testing
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作者 YIN Yichao HUANG Hongzhong LIU Zheng 《Journal of Shanghai Jiaotong university(Science)》 EI 2019年第6期805-810,共6页
We present a new nonparametric predictive inference(NPI)method using a power-normal model for accelerated life testing(ALT).Combined with the accelerating link function and imprecise probability theory,the proposed me... We present a new nonparametric predictive inference(NPI)method using a power-normal model for accelerated life testing(ALT).Combined with the accelerating link function and imprecise probability theory,the proposed method is a feasible way to predict the life of the product using ALT failure data.To validate the method,we run a series of simulations and conduct accelerated life tests with real products.The NPI lower and upper survival functions show the robustness of our method for life prediction.This is a continuous research,and some progresses have been made by updating the link function between different stress levels.We also explain how to renew and apply our model.Moreover,discussions have been made about the performance. 展开更多
关键词 accelerated life testing(ALT) power-normal model lower and upper survival functions nonparametric predictive inference(NPI) imprecise probability
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