A Statistical Model for Wind Power Forecast Error Based on Kernel Density Estimation

Liu, Liyang; Jun-ji, WU; Meng, Shaoliang

doi:10.2174/1874129001408010501

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Input data X are extracted by the feature layer into a series of feature nodes. The feature vector Z i of the i-th feature window is shown in Equation (12).…”

Section: Broad Learning Systemmentioning

confidence: 99%

“…In addition to constructing the model interval output directly by using the upper and lower boundary theory, the data interval can also be constructed indirectly in a probabilistic way based on point prediction. They are mainly divided into statistical probability interval prediction [12], quantile regression (QR) [13], bootstrap [14], and other probability predictions [15]. Statistical probability interval prediction is based on the known probability distribution and on construct interval predictions with confidence intervals according to the quantile.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wind Power Interval Prediction with Adaptive Rolling Error Correction Based on PSR-BLS-QR

Ran¹,

Chen²,

Ma³

et al. 2022

Energies

View full text Add to dashboard Cite

Effective prediction of wind power output intervals can capture the trend of uncertain wind output power in the form of probability, which not only can avoid the impact of randomness and volatility on grid security, but also can provide supportable information for grid dispatching and grid planning. To address the problem of the low accuracy of traditional wind power interval prediction, a new interval prediction method of wind power is proposed based on PSR-BLS-QR with adaptive rolling error correction. First, one-dimensional wind power data are mapped to high-dimensional space by phase space reconstruction (PSR) to achieve data reconstruction and the input and output of the broad learning system (BLS) model are constructed. Second, the training set and the test set are divided according to the input and output data. The BLS model is trained by the training set and the initial power interval of training data is constructed by quantile regression (QR). Then, the error distribution of nonparametric kernel density estimation is constructed at different power interval segments of the interval upper and lower boundaries, respectively, and the corresponding error-corrected power is found. Next, the optimal correction index is used as the objective function to determine the optimal error correction power for different power interval segments of the interval upper and lower boundaries. Finally, a test set is used for testing the performance of the proposed method. Three wind power datasets from different regions are used to prove that the proposed method can improve the average prediction accuracy by about 6–14% with the narrower interval width compared with the traditional interval prediction methods.

show abstract

“…Input data X are extracted by the feature layer into a series of feature nodes. The feature vector Z i of the i-th feature window is shown in Equation (12).…”

Section: Broad Learning Systemmentioning

confidence: 99%