Ultra-short-term combined prediction approach based on kernel function switch mechanism

Lu, Peng; Ye, Lin; Tang, Yong; Zhao, Yongning; Zhong, Wuzhi; Qu, Ying; Zhai, Bingxu

doi:10.1016/j.renene.2020.09.110

Cited by 38 publications

(9 citation statements)

References 51 publications

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“…Yang et al [116] and Gao et al [117] employed the differential evolution-grey wolf optimizer (DE-GWO) and fractional-order beetle swarm optimization (FO-BSO), respectively, to adjust the hyperparameters of LSSVM. In [118], an improved cuckoo search (ICS) was established to optimize the penalty factor and kernel function parameters of LSSVM. Additionally, Ding et al [96] utilized the WOA to generate optimal initial parameters for KELM, contributing to the accurate short-term prediction of wind power.…”

Section: Parameter Optimizationmentioning

confidence: 99%

“…Additionally, introducing advanced metaheuristic algorithms or deep learning techniques, such as adaptive learning rates and batch normalization, may contribute to improving the convergence speed and stability of optimization results. GWO [93], CABC [23], DE-GWO [116], FO-BSO [117], ICS [118], WOA [96], ISOA [119], IHDEHHO [120], SCWCA [121] NN-based models ELM, BPNN, RNN, DBN, etc. Weights, bias, learning rate, etc.…”

Section: Parameter Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

Song,

Tan,

Huang

et al. 2024

Energies

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Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein.

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Section: Parameter Optimizationmentioning

confidence: 99%

Section: Parameter Optimizationmentioning

confidence: 99%

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

Song,

Tan,

Huang

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…It is impractical to obtain the optimal ARI MA θ * (p * , q * , d * ) model since structure determination as in (7) requires the optimal parameter θ * , while the parameter estimation (8) requires the optimal structure (p * , q * , d * ).…”

Section: Structure Of the Linear Modelmentioning

confidence: 99%

“…Effectively handling these issues is crucial for reliable results [6]. Various methods have been developed to address missing values, categorized into two groups: physical and statistical approaches [7]. These methods can be applied across different time scales for wind energy forecasting, with statistical methods primarily used for complete datasets and physical methods for those with missing values [8].…”

Section: Introductionmentioning

confidence: 99%

Time Series Forecasting with Missing Values Using Multiple Models: An Application to Wind Energy Systems

2024

Preprint

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When dealing with limited sample sizes or missing values in time series data, achieving high accuracy in forecasting becomes challenging. This challenge is particularly common in wind energy forecasting, where many wind farms lack complete data. In this paper, we employ the widely-used ARIMA (Autoregressive Integrated Moving Average) model for time series forecasting. To address the difficulties posed by limited sample sizes and missing values, we adopt a unique approach by utilizing multiple ARIMA models for different datasets. A key distinction from other multiple models methods for time series forecasting lies in each model within our approach using its own dataset, making the fusion process more intricate. To overcome this, we incorporate neural networks and two techniques for model fusion: direct fusion with a learning mechanism and compensation for missing data through training. For each ARIMA model, we introduce a noise estimation method to circumvent the white noise assumptions inherent in ARIMA models. Finally, we apply these methods to wind energy prediction, and our experimental results showcase a significant enhancement in forecasting accuracy.

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“…The original wind power is decomposed by the CEEMDAN to eliminate the noise of the data. Then, the decomposed wind power is reconstructed into new subsequences (Lu et al, 2021). In addition, to improve the prediction accuracy, the end effect of the intrinsic mode functions (IMFs) obtained by CEEMDAN also needs to be resolved (Huang et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Regional Ultra-Short-Term Wind Power Combination Prediction Method Based on Fluctuant/Smooth Components Division

Yan

He³

et al. 2022

Front. Energy Res.

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When multiple scattered wind farms are connected to the power grid, the meteorological and geographic information data used for power prediction of a single wind farm are not suitable for the regional wind power prediction of the dispatching department. Therefore, based on the regional wind power historical data, this study proposes a combined prediction method according to data decomposition. Firstly, the original sequence processed by the extension methods is decomposed into several regular components by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). All the components are classified into two categories: fluctuant components and smooth components. Then, according to the characteristics of different data, the long short-term memory (LSTM) network and autoregressive integrated moving average (ARIMA) model are used to model the fluctuant components and the smooth components, respectively, and obtain the predicted values of each component. Finally, the predicted data of all components are accumulated, which is the final predicted result of the regional ultra-short-term wind power. The feasibility and accuracy of this method are verified by the comparative analysis.

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Ultra-short-term combined prediction approach based on kernel function switch mechanism

Cited by 38 publications

References 51 publications

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

Time Series Forecasting with Missing Values Using Multiple Models: An Application to Wind Energy Systems

Regional Ultra-Short-Term Wind Power Combination Prediction Method Based on Fluctuant/Smooth Components Division

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