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contributor authorBo, Gu
contributor authorKeke, Luo
contributor authorHongtao, Zhang
contributor authorJinhua, Zhang
contributor authorHui, Huang
date accessioned2022-02-05T22:01:31Z
date available2022-02-05T22:01:31Z
date copyright4/9/2021 12:00:00 AM
date issued2021
identifier issn0199-6231
identifier othersol_143_5_054503.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4276763
description abstractAccurate forecasting is the key factor in promoting wind power consumption and improving the stable operation of power systems. A short-term wind power forecasting (WPF) and uncertainty analysis method based on whale optimization algorithm (WOA), least squares support vector machine (LSSVM), and nonparametric kernel density estimation (NPKDE) was proposed in this paper. The advantages of WOA (fast convergence speed and high convergence accuracy) were used to optimize the penalty factor and kernel function width of the LSSVM model, and the calculation speed and forecasting accuracy of the LSSVM model were improved. The training sample set is classified according to the wind speed interval, and the WOA-LSSVM forecasting model is trained by subclass after classification to further improve the accuracy of short-term WPF. The NPKDE method is used to accurately calculate the probability density distribution characteristics of the forecasting error of wind power, and the confidence interval of the WPF is accurately calculated based on the probability density distribution characteristics. The calculation results show that the forecasting accuracy of the WOA-LSSVM model is higher than those of the LSSVM, long short-term memory (LSTM), and particle swarm optimization and least squares support vector machine (PSO-LSSVM) models, and the forecasting accuracy of the WOA-LSSVM model can be further improved after classifying the training sample set. The coverage of the confidence intervals in different time scales is higher than the corresponding confidence level, indicating that the NPKDE method can accurately describe the probability density distribution characteristics of the WPF errors.
publisherThe American Society of Mechanical Engineers (ASME)
titleShort-Term Forecasting and Uncertainty Analysis of Wind Power
typeJournal Paper
journal volume143
journal issue5
journal titleJournal of Solar Energy Engineering
identifier doi10.1115/1.4050594
journal fristpage054503-1
journal lastpage054503-7
page7
treeJournal of Solar Energy Engineering:;2021:;volume( 143 ):;issue: 005
contenttypeFulltext


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