A novel ultra-short-term wind power prediction method based on XA mechanism

Peng, Cheng; Zhang, Yiqin; Zhang, Bowen; Song, Dan; Lyu, Yi; Tsoi, AhChung

doi:10.1016/j.apenergy.2023.121905

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…The formula for calculating the value of attention on node i based on the features of node j is shown in Equation (6), where e ij is the attention score between node i and node j, a represents the correlation calculation function between nodes, h j is the output vector of node i, and W is the weight.…”

Section: Graph Attentionmentioning

confidence: 99%

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ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution

Peng,

Guo,

Huang

et al. 2024

Applied Sciences

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The integration of large-scale wind power into the power grid threatens the stable operation of the power system. Traditional wind power prediction is based on time series without considering the variability between wind turbines in different locations. This paper proposes a wind power probability density prediction method based on a time-variant deep feed-forward neural network (ForecastNet) considering a spatio-temporal distribution. First, the outliers in the wind turbine data are detected based on the isolated forest algorithm and repaired through Lagrange interpolation. Then, based on the graph attention mechanism, the features of the proximity node information of the individual wind turbines in the wind farm are extracted and the input feature matrix is constructed. Finally, the wind power probability density prediction results are obtained using the ForecastNet model based on three different hidden layer variants. The experimental results show that the ForecastNet model with a hidden layer as a dense network based on the attention mechanism (ADFN) predicts better. The average width of the prediction intervals at achieved confidence levels for all interval coverage is reduced by 34.19%, 35.41%, and 35.17%, respectively, when compared to the model with the hidden layer as a multilayer perceptron. For different categories of wind turbines, ADFN also achieves relatively narrow interval average widths of 368.37 kW, 315.87 kW, and 299.13 kW, respectively.

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Section: Graph Attentionmentioning

confidence: 99%

“…Existing wind power prediction methods are categorized into ultra-short-term prediction [5,6], short-term prediction [7,8] and mid-to-long-term prediction [9,10], with multiple prediction time dimensions. Commonly used wind power prediction models include physical [11][12][13][14] and statistical [15][16][17][18] methods.…”

Section: Introductionmentioning

confidence: 99%

ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution

Peng,

Guo,

Huang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Any day-ahead planning framework for a WHHPS is predicated on the forecast of the wind power prediction. A lot of research has been devoted to improving the forecast accuracy of wind power especially for short-term wind power prediction [12] [13]. Even though more precise methods are proposed, the prediction error is still systemic and inherent in these methods [14] and some researchers try to learn from the historical prediction errors and make necessary modifications on the prediction results to improve the prediction [15].…”

Section: Literature Reviewmentioning

confidence: 99%