Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

Lian, Lian; He, Kan

doi:10.1177/0309524x211056822

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…The use of artificial intelligence (AI) models in wind power forecasting has become increasingly prevalent with the advancement of computer science and technology. Various AI-based models such as artificial neural networks [10], extreme learning machine [11], fuzzy logic models [12], and support vector machines [13] have been successfully applied. Among these models, deep learning has gained significant attention in wind power forecasting due to its feature extraction and nonlinear fitting capabilities [14,15].…”

Section: Introductionmentioning

confidence: 99%

Multiple decomposition‐aided long short‐term memory network for enhanced short‐term wind power forecasting

Balci,

Dokur,

Yuzgec

et al. 2023

IET Renewable Power Gen

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With the increasing penetration of grid‐scale wind energy systems, accurate wind power forecasting is critical to optimizing their integration into the power system, ensuring operational reliability, and enabling efficient system asset utilization. Addressing this challenge, this study proposes a novel forecasting model that combines the long‐short‐term memory (LSTM) neural network with two signal decomposition techniques. The EMD technique effectively extracts stable, stationary, and regular patterns from the original wind power signal, while the VMD technique tackles the most challenging high‐frequency component. A deep learning‐based forecasting model, i.e. the LSTM neural network, is used to take advantage of its ability to learn from longer sequences of data and its robustness to noise and outliers. The developed model is evaluated against LSTM models employing various decomposition methods using real wind power data from three distinct offshore wind farms. It is shown that the two‐stage decomposition significantly enhances forecasting accuracy, with the proposed model achieving R2 values up to 9.5% higher than those obtained using standard LSTM models.

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Section: Introductionmentioning

confidence: 99%

Multiple decomposition‐aided long short‐term memory network for enhanced short‐term wind power forecasting

Balci,

Dokur,

Yuzgec

et al. 2023

IET Renewable Power Gen

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show abstract

“…In ref. [18], the SMA was used to optimise the random parameters of the ELM network, which optimised the recognition rate of the model to a certain extent. The harris hawks optimisation (HHO) is a more complex meta-heuristic algorithm inspired by the preying habits of the Harris eagle on rabbits.…”

Section: Introductionmentioning

confidence: 99%

Improved hybrid sparrow search algorithm for an extreme learning machine neural network for short‐term photovoltaic power prediction in 5G energy‐routing base stations

Yan

Guo

Hu³

et al. 2022

IET Renewable Power Gen

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Given the advancements in solar power generation and fifth-generation (5G) technologies, it is crucial to reduce energy consumption based on accurate predictions of the photovoltaic power requirements of 5G base stations (BSs) connected to renewable energy sources. To ensure sufficient prediction performance even under complex weather conditions and promote two-way flow of energy and information in the power supply system of the 5G BS, this work first proposes a reference scenario for a 5G BS with an energy router. Then, the comprehensive grey relational analysis is used to determine the optimal photovoltaic input variables. Finally, the improved logistic distribution, Laplacian distribution with inverse incomplete gamma-function weight factor, and nonlinear mutation perturbation strategy are introduced to improve the ability of the sparrow search algorithm to avoid locally optimal solutions for an extreme learning machine of the short-term photovoltaic power prediction model. The proposed model is applied to actual measured data for validation, and the results show that this model has the lowest mean percentage error as well as highest prediction accuracy under different weather conditions. The coefficient of determination for the proposed prediction model is greater than 99%; therefore, it is expected to improve the photovoltaic prediction performances of 5G energy-routing BSs as well as promote sustainable development and sharing of energy and information.

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A novel BiGRU multi-step wind power forecasting approach based on multi-label integration random forest feature selection and neural network clustering

Jiang,

Tan,

et al. 2024

Energy Conversion and Management

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Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

Cited by 5 publications

References 33 publications

Multiple decomposition‐aided long short‐term memory network for enhanced short‐term wind power forecasting

Multiple decomposition‐aided long short‐term memory network for enhanced short‐term wind power forecasting

Improved hybrid sparrow search algorithm for an extreme learning machine neural network for short‐term photovoltaic power prediction in 5G energy‐routing base stations

A novel BiGRU multi-step wind power forecasting approach based on multi-label integration random forest feature selection and neural network clustering

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