English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Wind power is a new energy source, for which the forecasting accuracy is related to the feasibility of various decision-making schemes for the power grid. The random, fluctuating and uncertain characteristics of wind power make accurate point forecasting very difficult and result in low practical application value. Therefore, a novel wind-power interval prediction method is proposed in this paper. First, variational model decomposition (VMD) is applied to the historical time sequence for wind power, followed by using an extreme learning machine (ELM) to establish a prediction model for the intrinsic mode function (IMF). An autoregressive integrated moving average (ARIMA) is used to establish a prediction model for the residual components, and the predicted IMF and residual components are reconstructed to predict the wind power. Finally, iterative self-organizing data analysis technique algorithm (ISODATA) are used with the error sequence to perform clustering segmentation, and adaptive diffusion kernel density estimation (ADKDE) is used to fit the probability density function for the prediction error of each segment and thereby obtain the cumulative distribution function and the final interval prediction under a given confidence level (CL). The results of a simulation based on real wind farm data from Hunan Province, China, from January to April 2020 show that the proposed method can significantly improve interval coverage.

Interval Prediction Method for Wind Power Based on VMD-ELM/ARIMA-ADKDE