RETRACTED ARTICLE: New SVM kernel soft computing models for wind speed prediction in renewable energy applications

Natarajan, Yogambal Jayalakshmi; Nachimuthu, Deepa Subramaniam

doi:10.1007/s00500-019-04608-w

Cited by 28 publications

(10 citation statements)

References 26 publications

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“…Additionally, further research into the implications of renewable penetration EU and national policies is needed (an example for the case of Lithuania can be seen in Gaigalis and Katinas [49]). Furthermore, AI-based methods such as SVM kernel soft computing models [50] and deep echo state network [51] should be considered in future research for their promising results and potential, as they seem to commonly outperform more traditional methods of renewable generation prediction. Moreover, further investigation is necessary to find the best approach for the risk definition while taking into account the inherent climate characteristics.…”

Section: Discussionmentioning

confidence: 99%

Predictable and Unpredictable Climate Variability Impacts on Optimal Renewable Energy Mixes: The Example of Spain

et al. 2020

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We analyzed the role of predictable and unpredictable variability in the identification of optimal renewable energy mixes in an electricity system. Renewable energy sources are the fastest growing energy generation technology, but the variable nature of production linked to climate variability raises structural, technological and economical issues. This work proposes the differentiation of the treatment applied to predictable and unpredictable variability in the context of Markowitz portfolio theory for optimal renewable deployment. The e4clim model was used as a tool to analyze the impact of predictable sources of generation variability on the optimal renewable energy mixes. Significant differences appeared, depending on the consideration of risk, all of them showing room for improvement with respect to the current situation. The application of the methods developed in this study is encouraged in mean-variance analyses, since its contribution favors scenarios where unpredictable variability in the climate-powered renewable energy sources are considered for their risk introduction.

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Section: Discussionmentioning

confidence: 99%

Predictable and Unpredictable Climate Variability Impacts on Optimal Renewable Energy Mixes: The Example of Spain

et al. 2020

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“…Basically, the principle of SVM is to learn a hyperplane in feature space to make the interval become maximal. Even though SVM predictions have been applied in many fields (Jianwei et al, 2019; Natarajan & Subramaniam, 2020), they have long training times because of the quadratic programming. Therefore, a novel improved SVM called the LSSVM was designed by Suykens and Vandewalle (1999) to turn the quadratic programming into a set of linear equations.…”

Section: Methodsmentioning

confidence: 99%

Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine

Zhang

2021

Journal of Forecasting

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In an attempt to combat global warming, many countries have been introducing carbon trading schemes, which, in turn, has led to increased research interest in carbon price forecasting as accurate predictions can provide valuable decision references for governments and investors. Therefore, this paper proposes an optimal hybrid forecasting framework to ensure accurate long‐term carbon price series predictions for which empirical mode decomposition (EMD) was used to decompose the original data and a novel optimal combination developed to predict each decomposed part, which involved the integration of autoregressive moving average‐type (ARMA‐type) models and least squares support vector machines (LSSVMs) optimized by 10‐fold cross validation and particle swarm optimization (PSO) algorithm. The effectiveness of the optimal EMD‐ARMAs‐LSSVMs framework was then verified using an empirical example from the EU emissions trading system (EU ETS). The long‐term forecast results and comparisons with other forecasting methods demonstrated the stability and validity of the proposed framework. The results indicated that ARMA‐type models are more suitable for low frequency and trend components, and LSSVMs were more suitable for high frequency components. It was also found that the proposed optimal framework was able to effectively reduce the error accumulation.

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“…For example, Ahmed, Khalid and Akram [22] used the SVM to predict the wind speed time-series data for some areas of Sindh, Pakistan. Natarajan and Nachimuthu [23] applied the SVM with the combination of other models to predict wind speed accurately. Zhu, Zhou and Fan [24] utilized SVM for the short-term wind speed investigation.…”

Section: A Backgroundmentioning

confidence: 99%

A New Hybrid ARAR and Neural Network Model for Multi-Step Ahead Wind Speed Forecasting in Three Regions of Pakistan

2020

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Wind is one of the most essential sources of clean, environmental friendly, socially constructive, economically beneficial, and renewable energy. To intuit the potential of this energy in a region the accurate wind speed modeling and forecasting are crucially important, even for planning, conversion of wind energy to electricity, energy trading, and reducing instability. However, accurate prediction is difficult due to intermittency and intrinsic complexity in wind speed data. This study aims to suggest a more appropriate model for accurate wind speed forecasting in the Jhimpir, Gharo, and Talhar, regions of Sindh, Pakistan. Therefore, the present study combined the Autoregressive-Autoregressive (ARAR) and Artificial Neural Network (ANN) models to propose a new hybrid ARAR-ANN model for better prediction by precisely capturing different patterns of the wind speed time-series data sets. The proposed hybrid model is efficient in modeling, reducing statistical errors, and forecasting the wind speed effectively. The performance of the proposed hybrid ARAR-ANN model is compared using three error-statistics and Nash-Sutcliffe efficiency-coefficient. The empirical results of the four performance indices fully demonstrated the superiority of the hybrid ARAR-ANN model than persistence model, ARAR, ANN and SVM. Indeed, the proposed model is an effective and feasible approach for wind speed forecasting.

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RETRACTED ARTICLE: New SVM kernel soft computing models for wind speed prediction in renewable energy applications

Cited by 28 publications

References 26 publications

Predictable and Unpredictable Climate Variability Impacts on Optimal Renewable Energy Mixes: The Example of Spain

Predictable and Unpredictable Climate Variability Impacts on Optimal Renewable Energy Mixes: The Example of Spain

Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine

A New Hybrid ARAR and Neural Network Model for Multi-Step Ahead Wind Speed Forecasting in Three Regions of Pakistan

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