Short-Term Wind Speed Prediction of Wind Farms Based on Particle Swarm Optimization Support Vector Machine

Yu, Xin; Zhang, Zhongxuan; Song, Mingxuan

doi:10.1088/1755-1315/804/3/032061

Cited by 5 publications

(2 citation statements)

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“…In its turn, Dong et al (2020) searched for the SVM parameters through an improved fireworks algorithm that can adaptively adjust the SVM parameters to get the best combinations in the solution space. The authors He and Fu (2021), Yu et al (2021) used PSO to conduct the model training on optimizing the SVM parameters. Wu (2019) used a GA with adaptive genetic operator rates to define the kernel parameters.…”

Section: Supervised Classification Methodsmentioning

confidence: 99%

“…Bat algorithm (Tharwat & Hassanien, 2019) CSOA (Moldovan, 2020) Differential evolution (Dixit et al, 2021) Fireworks algorithm (Dong et al, 2020) GA (Wu, 2019) Harris Hawks optimization (Houssein et al, 2021) Horse optimization algorithm (Moldovan et al, 2020) PSO (Moodi et al, 2021;Zhao et al, 2019;Yu et al, 2021;He & Fu, 2021; Social Ski Driver algorithm (Tharwat & Gabel, 2020) Although k-means is one of the most explored algorithms when it comes to clustering, some other methods and algorithms can also be used to solve clustering problems. For example, Kuo et al (2020) and Nguyen and Kuo (2019) used the Fuzzy c-means (FCM) algorithm, which is a clustering algorithm derived from the fuzzy set theory.…”

Section: Support Vector Machine Andmentioning

confidence: 99%

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Hybrid approaches to optimization and machine learning methods: a systematic literature review

Azevedo,

Rocha,

Pereira

2024

Mach Learn

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Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.

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Section: Supervised Classification Methodsmentioning

confidence: 99%

Section: Support Vector Machine Andmentioning

confidence: 99%

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Azevedo,

Rocha,

Pereira

2024

Mach Learn

View full text Add to dashboard Cite

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Predicting Wind Speed Using Optimized Long Short-Term Memory Neural Network

Ehteram

Seifi

Banadkooki

2023

Application of Machine Learning Models in Agricultural and Meteorological Sciences

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Adaptive soft sensor using stacking approximate kernel based BLS for batch processes

Zhao,

Yang,

et al. 2024

Sci Rep

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To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms.

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Short-Term Wind Speed Prediction of Wind Farms Based on Particle Swarm Optimization Support Vector Machine

Cited by 5 publications

References 0 publications

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Predicting Wind Speed Using Optimized Long Short-Term Memory Neural Network

Adaptive soft sensor using stacking approximate kernel based BLS for batch processes

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