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Haichao Wang
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Preprint
Published: 08 May 2018
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Accurate and stable cost forecasting of substation projects is of great significance to ensure the economic construction and sustainable operation of power engineering projects. In this paper, a forecasting model based on the improved least squares support vector machine (ILSSVM) optimized by wolf pack algorithm(WPA) is proposed to improve the accuracy and stability of the cost forecasting of substation projects. Firstly, the optimal features are selected through the data inconsistency rate (DIR), which helps reduce redundant input vectors. Secondly, the wolf pack algorithm is used to optimize the parameters of the improved least square support vector machine. Lastly, the cost forecasting method of WPA-DIR-ILSSVM is established. In this paper, 88 substation projects in different regions from 2015 to 2017 are chosen to conduct the training tests to verify the validity of the model. The results indicate that the new hybrid WPA-DIR-ILSSVM model presents better accuracy, robustness and generality in cost forecasting of substation projects.

ACS Style

Haichao Wang; Dongxiao Niu; Si Li; Fenghua Wang; Yi Liang. The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects. 2018, 1 .

AMA Style

Haichao Wang, Dongxiao Niu, Si Li, Fenghua Wang, Yi Liang. The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects. . 2018; ():1.

Chicago/Turabian Style

Haichao Wang; Dongxiao Niu; Si Li; Fenghua Wang; Yi Liang. 2018. "The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects." , no. : 1.

Journal article
Published: 05 December 2017 in Energies
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Accurate and stable prediction of icing thickness on transmission lines is of great significance for ensuring the safe operation of the power grid. In order to improve the accuracy and stability of icing prediction, an innovative prediction model based on the generalized regression neural network (GRNN) and the fruit fly optimization algorithm (FOA) is proposed. Firstly, a feature selection method based on the data inconsistency rate (IR) is adopted to select the optimal feature, which aims to reduce redundant input vectors. Then, the fruit FOA is utilized for optimization of smoothing factor for the GRNN. Lastly, the icing forecasting method FOA-IR-GRNN is established. Two cases in different locations and different months are selected to validate the proposed model. The results indicate that the new hybrid FOA-IR-GRNN model presents better accuracy, robustness, and generality in icing forecasting.

ACS Style

Dongxiao Niu; Haichao Wang; Hanyu Chen; Yi Liang. The General Regression Neural Network Based on the Fruit Fly Optimization Algorithm and the Data Inconsistency Rate for Transmission Line Icing Prediction. Energies 2017, 10, 2066 .

AMA Style

Dongxiao Niu, Haichao Wang, Hanyu Chen, Yi Liang. The General Regression Neural Network Based on the Fruit Fly Optimization Algorithm and the Data Inconsistency Rate for Transmission Line Icing Prediction. Energies. 2017; 10 (12):2066.

Chicago/Turabian Style

Dongxiao Niu; Haichao Wang; Hanyu Chen; Yi Liang. 2017. "The General Regression Neural Network Based on the Fruit Fly Optimization Algorithm and the Data Inconsistency Rate for Transmission Line Icing Prediction." Energies 10, no. 12: 2066.

Journal article
Published: 28 September 2017 in Sustainability
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China has now become the largest country in carbon emissions all over the world. Furthermore, with transportation accounting for an increasing proportion of CO2 emissions year by year, the transportation sector has turned out to be one of the main sectors which possesses a high growth speed in CO2 emissions. To accurately analyze potentially influencing factors which accelerate the process of CO2 emissions of transportation sector in China, based on carbon accounting by the checklists method of Intergovernmental Panel on Climate Change’s (IPCC), in this paper, we propose a decomposition model using Logarithmic Mean Divisia Index (LMDI) decomposition analysis technology and modified fixed growth rate method. Then effects of six influencing factors including energy structure, energy efficiency, transport form, transportation development, economic development and population size from 2001 to 2014 were quantitatively analyzed. Consequently, the results indicate that: (1) economic development accounts most for driving CO2 emissions growth of the transportation sector, while energy efficiency accounts most for suppressing CO2 emissions growth; (2) the pulling effects of natural gas, electricity and other clean energy consumption on CO2 emissions growth offset the inhibitory effects of traditional fossil fuels, making energy structure play a significant role in promoting CO2 emissions growth; (3) the inhibitory effects of railways and highways lead to inhibitory effects of transport form on CO2 emissions growth; (4) transportation development plays an obvious role in promoting CO2 emissions, while the effects of population size is relatively weaker compared with those of transportation development. Furthermore, the decomposition model of CO2 emissions factors in transport industry constructed in this paper can also be applied to other countries so as to provide guidance and reference for CO2 emissions analysis of transportation industry.

ACS Style

Yi Liang; Dongxiao Niu; Haichao Wang; Yan Li. Factors Affecting Transportation Sector CO2 Emissions Growth in China: An LMDI Decomposition Analysis. Sustainability 2017, 9, 1730 .

AMA Style

Yi Liang, Dongxiao Niu, Haichao Wang, Yan Li. Factors Affecting Transportation Sector CO2 Emissions Growth in China: An LMDI Decomposition Analysis. Sustainability. 2017; 9 (10):1730.

Chicago/Turabian Style

Yi Liang; Dongxiao Niu; Haichao Wang; Yan Li. 2017. "Factors Affecting Transportation Sector CO2 Emissions Growth in China: An LMDI Decomposition Analysis." Sustainability 9, no. 10: 1730.

Journal article
Published: 13 August 2017 in Energies
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Stable and accurate forecasting of icing thickness is of great significance for the safe operation of the power grid. In order to improve the robustness and accuracy of such forecasting, this paper proposes an innovative combination forecasting model using a modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) based on the variance-covariance (VC) weight determination method. Firstly, the initial weights and thresholds of BPNN are optimized by mind evolutionary computation (MEC) to prevent the BPNN from falling into local optima and speed up its convergence. Secondly, a bat algorithm (BA) is utilized to optimize the key parameters of SVM. Thirdly, the kernel function is introduced into an extreme learning machine (ELM) to improve the regression prediction accuracy of the model. Lastly, after adopting the above three modified models to predict, the variance-covariance weight determination method is applied to combine the forecasting results. Through performance verification of the model by real-world examples, the results show that the forecasting accuracy of the three individual modified models proposed in this paper has been improved, but the stability is poor, whereas the combination forecasting method proposed in this paper is not only accurate, but also stable. As a result, it can provide technical reference for the safety management of power grid.

ACS Style

Dongxiao Niu; Yi Liang; Haichao Wang; Meng Wang; Wei-Chiang Hong. Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method. Energies 2017, 10, 1196 .

AMA Style

Dongxiao Niu, Yi Liang, Haichao Wang, Meng Wang, Wei-Chiang Hong. Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method. Energies. 2017; 10 (8):1196.

Chicago/Turabian Style

Dongxiao Niu; Yi Liang; Haichao Wang; Meng Wang; Wei-Chiang Hong. 2017. "Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method." Energies 10, no. 8: 1196.

Journal article
Published: 18 January 2017 in Sustainability
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Industrial sectors account for around 70% of the total energy-related CO2 emissions in China. It is of great importance to measure the potential for CO2 emissions reduction and calculate the carbon price in industrial sectors covered in the Emissions Trading Scheme and carbon tax. This paper employs the directional distance function to calculate the marginal abatement costs of CO2 emissions during 2005–2011 and makes a comparative analysis between our study and the relevant literature. Our empirical results show that the marginal abatement costs vary greatly from industry to industry: high marginal abatement costs occur in industries with low carbon intensity, and vice versa. In the application of the marginal abatement cost, the abatement distribution scheme with minimum cost is established under different abatement targets. The conclusions of abatement distribution scheme indicate that those heavy industries with low MACs and high carbon intensity should take more responsibility for emissions reduction and vice versa. Finally, the policy implications for marginal abatement cost are provided.

ACS Style

Bowen Xiao; Dongxiao Niu; Han Wu; Haichao Wang. Marginal Abatement Cost of CO2 in China Based on Directional Distance Function: An Industry Perspective. Sustainability 2017, 9, 138 .

AMA Style

Bowen Xiao, Dongxiao Niu, Han Wu, Haichao Wang. Marginal Abatement Cost of CO2 in China Based on Directional Distance Function: An Industry Perspective. Sustainability. 2017; 9 (1):138.

Chicago/Turabian Style

Bowen Xiao; Dongxiao Niu; Han Wu; Haichao Wang. 2017. "Marginal Abatement Cost of CO2 in China Based on Directional Distance Function: An Industry Perspective." Sustainability 9, no. 1: 138.