Short-Term Wind Power Prediction Based on Empirical Mode Decomposition and Extreme Learning Machine

Wu, Jiajia; Liu, Changliang

doi:10.2991/emcpe-16.2016.173

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Because the single hidden layer map h(x) in extreme learning machine algorithm has the same effect as the kernel function mapping in SVM, this study replaced the single hidden layer map h(x) in extreme learning machine algorithm by Kernel function in SVM algorithm. When h(x) is unknown, the following nuclear matrix is defined according to the Mercer condition [36]:…”

Section: Primary Principal Of the Svm-elm Model For Carbon Emissions mentioning

confidence: 99%

Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm

Wang

et al. 2018

Energies

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Carbon emissions and environmental protection issues have brought pressure from the international community during Chinese economic development. Recently, Chinese Government announced that carbon emissions per unit of GDP would fall by 60–65% compared with 2005 and non-fossil fuel energy would account for 20% of primary energy consumption by 2030. The Beijing-Tianjin-Hebei region is an important regional energy consumption center in China, and its energy structure is typically coal-based which is similar to the whole country. Therefore, forecasting energy consumption related carbon emissions is of great significance to emissions reduction and upgrading of energy supply in the Beijing-Tianjin-Hebei region. Thus, this study thoroughly analyzed the main energy sources of carbon emissions including coal, petrol, natural gas, and coal power in this region. Secondly, the kernel function of the support vector machine was applied to the extreme learning machine algorithm to optimize the connection weight matrix between the original hidden layer and the output layer. Thirdly, the grey prediction theory was used to predict major energy consumption in the region from 2017 to 2030. Then, the energy consumption and carbon emissions data for 2000–2016 were used as the training and test sets for the SVM-ELM (Support Vector Machine-Extreme Learning Machine) model. The result of SVM-ELM model was compared with the forecasting results of SVM (Support Vector Machine Algorithm) and ELM (Extreme Learning Machine) algorithm. The accuracy of SVM-ELM was shown to be higher. Finally, we used forecasting output of GM (Grey Prediction Theory) (1, 1) as the input of the SVM-ELM model to predict carbon emissions in the region from 2017 to 2030. The results showed that the proportion of energy consumption seriously affects the amount of carbon emissions. We found that the energy consumption of electricity and natural gas will reach 45% by 2030 and carbon emissions in the region can be controlled below 96.9 million tons. Therefore, accelerating the upgradation of industrial structure will be the key task for the government in controlling the amount of carbon emissions in the next step.

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Section: Primary Principal Of the Svm-elm Model For Carbon Emissions mentioning

confidence: 99%

Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm

Wang

et al. 2018

Energies

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“…NN solves compound nonlinear difficult complications, which cannot be answered by the classic approaches. [28][29][30] Unseen Markov Mockup (HMM), support vector machine (SVM), and backpropagation are the numbers of commonly used methods algorithms in training the neural network. The NN defect is its requirement for a longer learning period within the Extreme Learning Machine (ELM) as the NN sole layer feeder.…”

mentioning

confidence: 99%

Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine

Band

Ghazvinei

Yusof

et al. 2020

Energy Science & Engineering

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Theoretically, the ocean has the potential to generate 20,000 TWh to 92 000 TWh of electricity. While as of 2012, our planet only requires 16 000 TWh of electricity. 1,2 A large portion of the marine energy continues to be unexploited because of the high cost involved in generating electricity. 3,4 Tidal current or tidal stream technologies had great strides improvement on the way to commercialization in the last decade. Tidal range technologies utilize a barrage to produce power from the elevation variance among low and high tide. The innovations made for tidal range power generation are tidal "reefs," "lagoons," and "fences," as well as low head tidal barrages. 5 There are three groups of tidal energy technologies:

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Enhanced Online Sequential Parallel Extreme Learning Machine and its Application in Remaining Useful Life Prediction of Integrated Modular Avionics

Gao

Zhang

et al. 2019

IEEE Access

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Integrated modular avionics is one of the most advanced systems which has been widely applied in modern aircraft. The performance of integrated modular avionics deeply impacts flight mission. Remaining useful life prediction is the critical manner which can efficiently improve the safety and reliability of aircraft. Since integrated modular avionics is a real-time system, the prediction algorithm should have fast learning speed to satisfy the real-time requirement. In this paper, an enhanced online sequential method is proposed to predict the remaining useful life of integrated modular avionics. Firstly, this paper proposes an online sequential network which is based on the architecture of parallel layer network. Secondly, to enhance the learning capability, this paper adopts the extreme learning machine denosing autoencoder to determine the input weights of the network. Thirdly, an adaptive forgetting factor is added to further improve the performance of the proposed method. The effectiveness and the superiority of the proposed method are verified in comparison with three online sequential algorithms on the standard datasets. Finally, a degradation model is built to depict the deteriorated process of integrated modular avionics. The prediction results confirms that the proposed method can effectively address remaining useful life of integrated modular avionics.INDEX TERMS Integrated modular avionics, parallel layer network, remaining useful life, degradation model.

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Short-Term Wind Power Prediction Based on Empirical Mode Decomposition and Extreme Learning Machine

Cited by 3 publications

References 5 publications

Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm

Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm

Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine

Enhanced Online Sequential Parallel Extreme Learning Machine and its Application in Remaining Useful Life Prediction of Integrated Modular Avionics

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