A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction

Wu, Qunli; Peng, Chenyang

doi:10.3390/en9080585

Cited by 23 publications

(16 citation statements)

References 34 publications

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“…For the training problem of LS-SVM, performance of the LS-SVM algorithm is influenced by the regularization parameter and the kernel parameter [33]. The grid search and cross validation approaches could be used to get the optimal parameters.…”

Section: Pattern Recognitionmentioning

confidence: 99%

Diagnosis of DC Bias in Power Transformers Using Vibration Feature Extraction and a Pattern Recognition Method

Zhou

et al. 2018

Energies

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DC bias is a great threat to the safe operation of power transformers. This paper deals with a new vibration-based technique to diagnose DC bias in power transformers. With this technique, the DC bias status of power transformers can be automatically recognized. The vibration variation process of a 500 kV autotransformer is tested under the influence of DC bias in the monopole trail operation stage of a ±800 kV HVDC transmission system. Comparison of transformer vibration under normal and DC-biased conditions is conducted. Three features are proposed and are validated by sensitivity analysis. The principal component analysis method is employed for feature de-correlation and dimensionality reduction. The least square support vector machine algorithm is used and verified successful in DC bias recognition. A remote on-line monitoring device based on the proposed algorithm is designed and applied in field DC bias diagnosis of power transformers. The suggested diagnostic algorithm and monitoring device could be useful in targeted DC bias control and improving the safe operation level of power transformers.

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Section: Pattern Recognitionmentioning

confidence: 99%

Diagnosis of DC Bias in Power Transformers Using Vibration Feature Extraction and a Pattern Recognition Method

Zhou

et al. 2018

Energies

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“…Weishang Guo [56] proposed a novel hybrid combining the Beveridge-Nelson decomposition method, fruit fly optimization algorithm, and LSSVM model for electricity price forecasting. Qunli Wu [57] applied the LSSVM model optimized by a cloud-based evolutionary algorithm for accurate wind power generation prediction. Wei Sun [58] constructed a novel hybrid model based on principal component analysis and LSSVM optimized by cuckoo search to forecast the daily PM 2.5 concentration, which presents a strong potential.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Energy-Related CO2 Emissions Employing a Novel SSA-LSSVM Model: Considering Structural Factors in China

2018

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Carbon dioxide (CO 2) emissions forecasting is becoming more important due to increasing climatic problems, which contributes to developing scientific climate policies and making reasonable energy plans. Considering that the influential factors of CO 2 emissions are multiplex and the relationships between factors and CO 2 emissions are complex and non-linear, a novel CO 2 forecasting model called SSA-LSSVM, which utilizes the Salp Swarm Algorithm (SSA) to optimize the two parameters of the least squares support sector machine (LSSVM) model, is proposed in this paper. The influential factors of CO 2 emissions, including the gross domestic product (GDP), population, energy consumption, economic structure, energy structure, urbanization rate, and energy intensity, are regarded as the input variables of the SSA-LSSVM model. The proposed model is verified to show a better forecasting performance compared with the selected models, including the single LSSVM model, the LSSVM model optimized by the particle swarm optimization algorithm (PSO-LSSVM), and the back propagation (BP) neural network model, on CO 2 emissions in China from 2014 to 2016. The comparative analysis indicates the SSA-LSSVM model is greatly superior and has the potential to improve the accuracy and reliability of CO 2 emissions forecasting. CO 2 emissions in China from 2017 to 2020 are forecast combined with the 13th Five-Year Plan for social, economic and energy development. The comparison of CO 2 emissions of China in 2020 shows that structural factors significantly affect CO 2 emission forecasting results. The average annual growth of CO 2 emissions slows down significantly due to a series of policies and actions taken by the Chinese government, which means China can keep the promise that greenhouse gas emissions will start to drop after 2030.

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“…The predicting result is used for online optimal operation of the power system. Wind power predicting methods usually include time series analysis method such as the autoregressive moving average (ARMA) model [9] and artificial intelligence method based on historical wind power data such as artificial neural network (ANN) [10] and support vector machine (SVM) [11]. Recently, a modified SVM, known as least squares-SVM (LSSVM), was successfully employed in wind power prediction [12].…”

Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Multistep Prediction of Wind Power Based on Representative Unit Method

Yang

Liu

Cui

et al. 2018

Mathematical Problems in Engineering

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With the continuous expansion of wind power grid scale, wind power prediction is an important means to reduce the adverse impact of large-scale grid integration on power grid: the higher prediction accuracy, the better safety, and economy of grid operation. The existing research shows that the quality of input sample data directly affects the accuracy of wind power prediction. By the analysis of measured power data in wind farms, this paper proposes an ultra-short-term multistep prediction model of wind power based on representative unit method, which can fully excavate data information and select reasonable data samples. It uses the similarity measure of time series in data mining, spectral clustering, and correlation coefficient to select the representative units. The least squares support vector machine (LSSVM) model is used as a prediction model for outputs of the representative units. The power of the whole wind farm is obtained by statistical upscaling method. And the number of representative units has a certain impact on prediction accuracy. The case study shows that this method can effectively improve the prediction accuracy, and it can be used as pretreatment method of data. It has a wide range of adaptability.

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A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction

Cited by 23 publications

References 34 publications

Diagnosis of DC Bias in Power Transformers Using Vibration Feature Extraction and a Pattern Recognition Method

Diagnosis of DC Bias in Power Transformers Using Vibration Feature Extraction and a Pattern Recognition Method

Forecasting Energy-Related CO2 Emissions Employing a Novel SSA-LSSVM Model: Considering Structural Factors in China

Ultra-Short-Term Multistep Prediction of Wind Power Based on Representative Unit Method

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