Wind Power Prediction Based on Kalman Filter and Non-parametric Kernel Density Estimation

Li, Daoqing; Hussain, Adil; Yu, Xiaodong; Liu, Shulin; Yu, Xuanzhou; Zhang, Kai

doi:10.1109/icpsasia52756.2021.9621598

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Among them, the KDE method has gained popularity due to its simplicity and ease of implementation, and the mathematical formula is shown as follows [ 24,41 ]

f (x) = \frac{1}{n} \sum_{i = 1}^{n} \frac{1}{h^{D}} k (\frac{x_{i} - x}{h})

where n is the number of samples,

h^{normalD}

is the volume of a hypercube with side length h , k is the kernel function, h is known as bandwidth, x means the prediction error, and

x_{i}

is the i th error value. [ 42 ]…”

Section: Methodsmentioning

confidence: 99%

“…where n is the number of samples,h D is the volume of a hypercube with side length h, k is the kernel function, h is known as bandwidth, x means the prediction error, and x i is the ith error value. [42] The common kernel function includes Gaussian, Cosine, Epanechnikov, etc. According to the reference, [43] we determined the Gaussian function as the kernel function.…”

Section: Prediction Interval Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Combination of Chaotic Harris Hawks Optimizer‐Stacking Model and Kernel Density Estimation Method for Pressing Force Prediction During Slab Sizing Press Process

Wen

Wang

et al. 2023

steel research int.

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During the slab sizing press (SP) process, the pressing force corresponds to the slab profile, which guides the production schedule design and the final profile control. To accomplish the prediction of pressing force for SP, an improved ensemble method based on chaotic Harris hawks optimizer (CHHO) and stacking is proposed. A mechanistic knowledge is introduced for feature selection that enhances the rationality of input features. Subsequently, 11 machine learning models are compared and 5 of them are selected as candidate learners for the stacking method. Based on the candidates learners, 8 stacking strategies are constructed, which the stacked model with extratree regressor, gradient boosted decision trees, and kernel ridge regression (KRR) as base‐learners and KRR as meta‐learner performs the best. The R2, MAE, mean square error, mean squared log error, and mean absolute percentage error for the test dataset are 0.9912, 0.0856, 0.0167, 0.0005, and 2.00%, respectively, and 95% of the prediction errors are less than 0.15 MN. Then, sensitivity analysis and predictive analysis based on Shapley Additive Explanations are performed to demonstrate the good alignment of the proposed model with physical reality. Furthermore, to cope with the complexity and uncertainty of the production process, the proposed CHHO‐stacking model and the kernel density estimation method are integrated to model the prediction interval.

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“…Among them, the KDE method has gained popularity due to its simplicity and ease of implementation, and the mathematical formula is shown as follows [ 24,41 ]

f (x) = \frac{1}{n} \sum_{i = 1}^{n} \frac{1}{h^{D}} k (\frac{x_{i} - x}{h})

where n is the number of samples,

h^{normalD}

is the volume of a hypercube with side length h , k is the kernel function, h is known as bandwidth, x means the prediction error, and

x_{i}

is the i th error value. [ 42 ]…”

Section: Methodsmentioning

confidence: 99%

Section: Prediction Interval Methodsmentioning

confidence: 99%

A Combination of Chaotic Harris Hawks Optimizer‐Stacking Model and Kernel Density Estimation Method for Pressing Force Prediction During Slab Sizing Press Process

Wen

Wang

et al. 2023

steel research int.

View full text Add to dashboard Cite

show abstract

A novel wind power prediction method of the lower upper bound evaluation based on GRU

Zha,

Zhang,

Dan

et al. 2024

Transactions of the Institute of Measurement and Control

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Compared with the point prediction of the wind power, the interval prediction provides more information to assist power dispatching and optimize quotation strategies in power trading. To improve the accuracy of the prediction intervals, this paper proposes a novel model based on the lower upper bound evaluation (LUBE) by the gate recurrent unit (GRU) network. First, the Pearson correlation coefficient is selected to screen out the variables related to wind power, which can simplify the input variables of the prediction model while preserving the feature information as much as possible. Afterwards, with the consideration of the prediction interval relative deviation ( PIRD), the improved objective function is introduced. Based on the GRU network, the prediction intervals of the wind power can be obtained to minimize the new objective function. Finally, by choosing several mainstream neural networks, experiments are conducted towards a certain wind farm in China. The results show that the proposed model has a significant improvement in both prediction interval width and PIRD under the given prediction interval coverage probability ( PICP).

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Short-Term Wind Power Prediction Using a Novel Model Based on Butterfly Optimization Algorithm-Variational Mode Decomposition-Long Short-Term Memory

Wang,

Zhao,

Hao

et al. 2023

Preprint

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Wind Power Prediction Based on Kalman Filter and Non-parametric Kernel Density Estimation

Cited by 3 publications

References 17 publications

A Combination of Chaotic Harris Hawks Optimizer‐Stacking Model and Kernel Density Estimation Method for Pressing Force Prediction During Slab Sizing Press Process

A Combination of Chaotic Harris Hawks Optimizer‐Stacking Model and Kernel Density Estimation Method for Pressing Force Prediction During Slab Sizing Press Process

A novel wind power prediction method of the lower upper bound evaluation based on GRU

Short-Term Wind Power Prediction Using a Novel Model Based on Butterfly Optimization Algorithm-Variational Mode Decomposition-Long Short-Term Memory

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