This page has only limited features, please log in for full access.

Unclaimed
Xiaomin Xu
Beijing Key Laboratory of New Energy Power and Low-Carbon Development, School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Review
Published: 13 January 2021 in Sustainability
Reads 0
Downloads 0

The green development of electric power is a key measure to alleviate the shortage of energy supply, adjust the energy structure, reduce environmental pollution and improve energy efficiency. Firstly, the situation and challenges of China’s power green development is analyzed. On this basis, the power green development models are categorized into two typical research objects, which are multi-energy synergy mode, represented by integrated energy systems, and multi-energy combination mode with clean energy participation. The key points of the green power development model with the consumption of new energy as the core are reviewed, and then China’s exploration of the power green development system and the latest research results are reviewed. Finally, the key scientific issues facing China’s power green development are summarized and put forward targeted countermeasures and suggestions.

ACS Style

Keke Wang; Dongxiao Niu; Min Yu; Yi Liang; Xiaolong Yang; Jing Wu; Xiaomin Xu. Analysis and Countermeasures of China’s Green Electric Power Development. Sustainability 2021, 13, 708 .

AMA Style

Keke Wang, Dongxiao Niu, Min Yu, Yi Liang, Xiaolong Yang, Jing Wu, Xiaomin Xu. Analysis and Countermeasures of China’s Green Electric Power Development. Sustainability. 2021; 13 (2):708.

Chicago/Turabian Style

Keke Wang; Dongxiao Niu; Min Yu; Yi Liang; Xiaolong Yang; Jing Wu; Xiaomin Xu. 2021. "Analysis and Countermeasures of China’s Green Electric Power Development." Sustainability 13, no. 2: 708.

Journal article
Published: 13 December 2020 in Journal of Cleaner Production
Reads 0
Downloads 0

As a low-cost, low-carbon, and clean renewable energy, hydropower is crucial to carbon emissions reduction and climate change mitigation. Compared with non-steady and highly volatile renewable energy, flexible hydropower can help secure the reliable operation of the grid. However, development cost increase, resettlement and environmental issues have recently hindered China’s hydropower development. This paper first reviews the history and current status of hydropower development in China, integrates the economy-energy-environment system, the water-energy-food nexus, and socio-hydrology theory to analyze the complex influence mechanism of hydropower development, and identifies driving factors of hydropower development through a cost-benefit analysis. Then, the quantitative system dynamics (SD) model of hydropower development is constructed based on the interdisciplinary qualitative analysis. Combining incentive policies and technological progress, four scenarios are set. The multi-scenario prediction results and sensitivity analysis results of the SD model indicate that incentive policies can enhance the attractiveness of hydropower investment and significantly promote long-term stable hydropower development, while technological progress can improve the efficiency and carbon emissions reduction benefits of hydropower. Based on the comparative analysis of these results, specific policy recommendations for promoting sustainable development of green hydropower in China are proposed in terms of four aspects: the hydropower development concept, cooperation mechanism, policy mechanism, and technological progress.

ACS Style

Lijie Sun; Dongxiao Niu; Keke Wang; Xiaomin Xu. Sustainable development pathways of hydropower in China: Interdisciplinary qualitative analysis and scenario-based system dynamics quantitative modeling. Journal of Cleaner Production 2020, 287, 125528 .

AMA Style

Lijie Sun, Dongxiao Niu, Keke Wang, Xiaomin Xu. Sustainable development pathways of hydropower in China: Interdisciplinary qualitative analysis and scenario-based system dynamics quantitative modeling. Journal of Cleaner Production. 2020; 287 ():125528.

Chicago/Turabian Style

Lijie Sun; Dongxiao Niu; Keke Wang; Xiaomin Xu. 2020. "Sustainable development pathways of hydropower in China: Interdisciplinary qualitative analysis and scenario-based system dynamics quantitative modeling." Journal of Cleaner Production 287, no. : 125528.

Journal article
Published: 15 November 2020 in Sustainability
Reads 0
Downloads 0

The inherent intermittency and uncertainty of wind power have brought challenges in accurate wind power output forecasting, which also cause tricky problems in the integration of wind power to the grid. In this paper, a hybrid deep learning model bidirectional long short term memory-convolutional neural network (BiLSTM-CNN) is proposed for short-term wind power forecasting. First, the grey correlation analysis is utilized to select the inputs for forecasting model; Then, the proposed hybrid model extracts multi-dimension features of inputs to predict the wind power from the temporal-spatial perspective, where the Bi-LSTM model is utilized to mine the bidirectional temporal characteristics while the convolution and pooling operations of CNN are utilized to extract the spatial characteristics from multiple input time series. Lastly, a case study is conducted to verify the superiority of the proposed model. Other deep learning models (Bi-LSTM, LSTM, CNN, LSTM-CNN, CNN-BiLSTM, CNN-LSTM) are also simulated to conduct comparison from three aspects. The results show that the BiLSTM-CNN model has the best accuracy with the lowest RMSE of 2.5492, MSE of 6.4984, MAE of 1.7344 and highest R2 of 0.9929. CNN has the fastest speed with an average computational time of 0.0741s. The hybrid model that mines the spatial feature based on the extracted temporal feature has a better performance than the model mines the temporal feature based on the extracted spatial feature.

ACS Style

Hao Zhen; Dongxiao Niu; Min Yu; Keke Wang; Yi Liang; Xiaomin Xu. A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction. Sustainability 2020, 12, 9490 .

AMA Style

Hao Zhen, Dongxiao Niu, Min Yu, Keke Wang, Yi Liang, Xiaomin Xu. A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction. Sustainability. 2020; 12 (22):9490.

Chicago/Turabian Style

Hao Zhen; Dongxiao Niu; Min Yu; Keke Wang; Yi Liang; Xiaomin Xu. 2020. "A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction." Sustainability 12, no. 22: 9490.

Research article
Published: 11 August 2020 in Journal of Advanced Transportation
Reads 0
Downloads 0

Considering that the charging behaviors of users of electric vehicles (EVs) (including charging time and charging location) are random and uncertain and that the disorderly charging of EVs brings new challenges to the power grid, this paper proposes an optimal electricity pricing strategy for EVs based on region division and time division. Firstly, by comparing the number of EVs and charging stations in different districts of a city, the demand ratio of charging stations per unit is calculated. Secondly, according to the demand price function and the principle of profit maximization, the charging price between different districts of a city is optimized to guide users to charge in districts with more abundant charging stations. Then, based on the results of the zonal pricing strategy, the time-of-use (TOU) pricing strategy in different districts is discussed. In the TOU pricing model, consumer satisfaction, the profit of power grid enterprises, and the load variance of the power grid are considered comprehensively. Taking the optimization of the comprehensive index as the objective function, the TOU pricing optimization model of EVs is constructed. Finally, the nondominated sorting genetic algorithm (NSGA-II) is introduced to solve the above optimization problems. The specific data of EVs in a municipality directly under the Central Government are taken as examples for this analysis. The empirical results demonstrate that the peak-to-valley ratio of a certain day in the city is reduced from 56.8% to 43% by using the optimal pricing strategy, which further smooth the load curve and alleviates the impact of load fluctuation. To a certain extent, the problem caused by the uneven distribution of electric vehicles and charging stations has been optimized. An orderly and reasonable electricity pricing strategy can guide users to adjust charging habits, to ensure grid security, and to ensure the economic benefits of all parties.

ACS Style

Xiaomin Xu; Dongxiao Niu; Yan Li; Lijie Sun. Optimal Pricing Strategy of Electric Vehicle Charging Station for Promoting Green Behavior Based on Time and Space Dimensions. Journal of Advanced Transportation 2020, 2020, 1 -16.

AMA Style

Xiaomin Xu, Dongxiao Niu, Yan Li, Lijie Sun. Optimal Pricing Strategy of Electric Vehicle Charging Station for Promoting Green Behavior Based on Time and Space Dimensions. Journal of Advanced Transportation. 2020; 2020 ():1-16.

Chicago/Turabian Style

Xiaomin Xu; Dongxiao Niu; Yan Li; Lijie Sun. 2020. "Optimal Pricing Strategy of Electric Vehicle Charging Station for Promoting Green Behavior Based on Time and Space Dimensions." Journal of Advanced Transportation 2020, no. : 1-16.

Journal article
Published: 06 June 2020 in Sustainability
Reads 0
Downloads 0

With the continuous increase in new energy installed capacity, the slowdown in the growth of social power consumption, the pressure created by high coal prices, and the reduction in on-grid electricity tariffs, the challenges facing the survival and development of thermal power generation enterprises are becoming more severe. Hence, based on the cost–benefit analysis method, this paper proposes a diversified operating benefit analysis and decision model for thermal power generation enterprises that includes four profit models: power sales, peak load regulation (without oil), peak load regulation (with oil), and generation right trading. The opportunity cost of peak load regulation and generation rights trading was considered, and six scenarios were designed. An empirical analysis was conducted by selecting a thermal power enterprise in Ningxia, Northwest China, as an example, using scenario and sensitivity analyses. The results show that under the diversified business model, thermal power generation enterprises can more effectively avoid the risks when the external environment changes and significantly improve its economic benefits. The consumption of new energy can be promoted, and positive social effects will be achieved. Therefore, the findings will help the thermal power generation enterprises to face these challenges.

ACS Style

Xiaolong Yang; Dongxiao Niu; Meng Chen; Keke Wang; Qian Wang; Xiaomin Xu. An Operation Benefit Analysis and Decision Model of Thermal Power Enterprises in China against the Background of Large-Scale New Energy Consumption. Sustainability 2020, 12, 4642 .

AMA Style

Xiaolong Yang, Dongxiao Niu, Meng Chen, Keke Wang, Qian Wang, Xiaomin Xu. An Operation Benefit Analysis and Decision Model of Thermal Power Enterprises in China against the Background of Large-Scale New Energy Consumption. Sustainability. 2020; 12 (11):4642.

Chicago/Turabian Style

Xiaolong Yang; Dongxiao Niu; Meng Chen; Keke Wang; Qian Wang; Xiaomin Xu. 2020. "An Operation Benefit Analysis and Decision Model of Thermal Power Enterprises in China against the Background of Large-Scale New Energy Consumption." Sustainability 12, no. 11: 4642.

Journal article
Published: 06 June 2020 in Sustainability
Reads 0
Downloads 0

With the deteriorating ecological environment and increasing energy consumption, developing clean and renewable energy sources has become a key measure to solve environmental problems and energy shortages. The multicriteria decision-making (MCDM) technique is widely used in the assessment of renewable energy alternatives (REA) to determine the most sustainable and appropriate option for a country or region. Classic REA ranking is conducted in a deterministic environment through MCDM techniques. However, with the increasing complexity of environmental and energy issues, the REA ranking method is unsuitable for use in today’s China. Therefore, in this paper, a fuzzy MCDM technique based on the interval-valued hesitant fuzzy elimination and choice expressing reality (IVHF-ELECTRE II) method, taking into account the uncertainty and ambiguity of the information, is proposed for REA ranking. A case study in China is conducted to elaborate on the rationality and feasibility of the proposed framework. According to the ranking results, hydro is determined as the best REA in China, followed by wind energy, solar photovoltaic, geothermal, biomass energy, and solar thermal. This research provides a feasible method and insightful reference for national decision-makers to utilize when evaluating the REA and establishing a macroplanning policy for renewable energy under an uncertain environment.

ACS Style

Dongxiao Niu; Hao Zhen; Min Yu; Keke Wang; Lijie Sun; Xiaomin Xu. Prioritization of Renewable Energy Alternatives for China by Using a Hybrid FMCDM Methodology with Uncertain Information. Sustainability 2020, 12, 1 .

AMA Style

Dongxiao Niu, Hao Zhen, Min Yu, Keke Wang, Lijie Sun, Xiaomin Xu. Prioritization of Renewable Energy Alternatives for China by Using a Hybrid FMCDM Methodology with Uncertain Information. Sustainability. 2020; 12 (11):1.

Chicago/Turabian Style

Dongxiao Niu; Hao Zhen; Min Yu; Keke Wang; Lijie Sun; Xiaomin Xu. 2020. "Prioritization of Renewable Energy Alternatives for China by Using a Hybrid FMCDM Methodology with Uncertain Information." Sustainability 12, no. 11: 1.

Journal article
Published: 11 May 2020 in Applied Soft Computing
Reads 0
Downloads 0

To mitigate solar curtailment caused by large-scale development of photovoltaic (PV) power generation, accurate forecasting of PV power generation is important. A hybrid forecasting model was constructed that combines random forest (RF), improved grey ideal value approximation (IGIVA), complementary ensemble empirical mode decomposition (CEEMD), the particle swarm optimization algorithm based on dynamic inertia factor (DIFPSO), and backpropagation neural network (BPNN), called RF-CEEMD-DIFPSO-BPNN. PV power generation is affected by many factors. The RF method is used to calculate the importance degree and rank the factors, then eliminate the less important factors. Then, the importance degree calculated by RF is transferred as the weight values to the IGIVA model to screen the similar days of different weather types to improve the data quality of the training sets. Then, the original power sequence is decomposed into intrinsic mode functions (IMFs) at different frequencies and a residual component by CEEMD to weaken the fluctuation of the original sequence. We empirically analyzed a PV power plant to verify the effectiveness of the hybrid model, which proved that the RF-CEEMD-DIFPSO-BPNN is a promising approach in terms of PV power generation forecasting.

ACS Style

Dongxiao Niu; Keke Wang; Lijie Sun; Jing Wu; Xiaomin Xu. Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study. Applied Soft Computing 2020, 93, 106389 .

AMA Style

Dongxiao Niu, Keke Wang, Lijie Sun, Jing Wu, Xiaomin Xu. Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study. Applied Soft Computing. 2020; 93 ():106389.

Chicago/Turabian Style

Dongxiao Niu; Keke Wang; Lijie Sun; Jing Wu; Xiaomin Xu. 2020. "Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study." Applied Soft Computing 93, no. : 106389.

Journal article
Published: 03 January 2020 in Energy Policy
Reads 0
Downloads 0

The “13th Five-Year Plan” for wind power has proposed that it will reach grid parity and compete with power and hydropower. Accordingly, many doubts have been raised. Is the wind power in China already equipped with conditions for grid parity? What is the impact on the development of wind power? To solve these doubts, this study employs a system dynamics model to judge whether China can achieve grid parity for wind power. First, the factor indicator system is constructed from the aspects of wind power production, consumption, and curtailment. Second, the trend of wind power curtailment, cost, revenue, and installed capacity are predicted from 2005 to 2030. Third, three scenarios are set to simulate the impact of grid parity on wind power. Empirical results show that: (1) Net revenue and installed capacity will continue to increase, while the wind power curtailment will gradually reduce. (2) When the subsidy is decreased to 0, revenue will significantly reduce, and the installed capacity will reduce by nearly 1/4. (3) The Chinese government should not abolish all subsidies for wind power to achieve grid parity in 2020. To prompt the process for the grid parity of wind power, some policy implications are proposed.

ACS Style

Xiaomin Xu; Dongxiao Niu; Bowen Xiao; Xiaodan Guo; Lihui Zhang; Keke Wang. Policy analysis for grid parity of wind power generation in China. Energy Policy 2020, 138, 111225 .

AMA Style

Xiaomin Xu, Dongxiao Niu, Bowen Xiao, Xiaodan Guo, Lihui Zhang, Keke Wang. Policy analysis for grid parity of wind power generation in China. Energy Policy. 2020; 138 ():111225.

Chicago/Turabian Style

Xiaomin Xu; Dongxiao Niu; Bowen Xiao; Xiaodan Guo; Lihui Zhang; Keke Wang. 2020. "Policy analysis for grid parity of wind power generation in China." Energy Policy 138, no. : 111225.

Journal article
Published: 11 November 2019 in Processes
Reads 0
Downloads 0

Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing strategy and improved extreme learning machine with kernel (KELM) is proposed, which mainly includes the following stages. Firstly, the Pearson correlation coefficient is calculated to determine the correlation degree between multiple factors of wind power to reduce data redundancy. Then, the complementary ensemble empirical mode decomposition (CEEMD) method is adopted to decompose the wind power time series to decrease the non-stationarity, the sample entropy (SE) theory is used to classify and reconstruct the subsequences to reduce the complexity of computation. Finally, the KELM optimized by harmony search (HS) algorithm is utilized to forecast each subsequence, and after integration processing, the forecasting results are obtained. The CEEMD-SE-HS-KELM forecasting model constructed in this paper is used in the short-term wind power forecasting of a Chinese wind farm, and the RMSE and MAE are as 2.16 and 0.39 respectively, which is better than EMD-SE-HS-KELM, HS-KELM, KELM and extreme learning machine (ELM) model. According to the experimental results, the hybrid method has higher forecasting accuracy for short-term wind power forecasting.

ACS Style

Keke Wang; Dongxiao Niu; Lijie Sun; Hao Zhen; Jian Liu; Gejirifu De; Xiaomin Xu. Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method. Processes 2019, 7, 843 .

AMA Style

Keke Wang, Dongxiao Niu, Lijie Sun, Hao Zhen, Jian Liu, Gejirifu De, Xiaomin Xu. Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method. Processes. 2019; 7 (11):843.

Chicago/Turabian Style

Keke Wang; Dongxiao Niu; Lijie Sun; Hao Zhen; Jian Liu; Gejirifu De; Xiaomin Xu. 2019. "Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method." Processes 7, no. 11: 843.

Journal article
Published: 24 September 2019 in Journal of Cleaner Production
Reads 0
Downloads 0

To determine whether China can achieve the commitment of reducing carbon emission intensity in 2030, a general regression neural network (GRNN) forecasting model based on improved fireworks algorithm (IFWA) optimization is constructed to forecast total carbon emissions (TCE) and carbon emissions intensity (CEI) in 2016–2040. Random forests (RF) method is used to select the important carbon emissions influencing factors to reduce data redundancy. The superiority of IFWA-GRNN forecasting model is verified by historical data from 1990 to 2015. The basic as usual (BAU), policy tightening (PT) and market allocation (ML) scenarios are set to forecast the TCE and CEI. The results show that under the BAU scenario, China’s CEI reduction commitments in 2020 (40%–45%) can be achieved, but the commitment in 2030 (60%–65%) cannot be achieved. Under the PT and ML scenarios, China can achieve its CEI commitments in 2030, and the TCE will decrease gradually after reaching its peak in 2030. Under the existing macro development planning and policy intensity in China, there are still certain pressures to achieve CEI reduction targets. It is necessary to implement policy adjustment and market mechanism incentives for both energy supply and consumption, optimize power supply structure, promote electric energy substitution, and accelerate the construction of a unified national electricity market, carbon market, etc.

ACS Style

Dongxiao Niu; Keke Wang; Jing Wu; Lijie Sun; Yi Liang; Xiaomin Xu; Xiaolong Yang. Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network. Journal of Cleaner Production 2019, 243, 118558 .

AMA Style

Dongxiao Niu, Keke Wang, Jing Wu, Lijie Sun, Yi Liang, Xiaomin Xu, Xiaolong Yang. Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network. Journal of Cleaner Production. 2019; 243 ():118558.

Chicago/Turabian Style

Dongxiao Niu; Keke Wang; Jing Wu; Lijie Sun; Yi Liang; Xiaomin Xu; Xiaolong Yang. 2019. "Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network." Journal of Cleaner Production 243, no. : 118558.

Journal article
Published: 14 November 2017 in Energies
Reads 0
Downloads 0

With the increase in energy demand, extreme climates have gained increasing attention. Ice disasters on transmission lines can cause gap discharge and icing flashover electrical failures, which can lead to mechanical failure of the tower, conductor, and insulators, causing significant harm to people’s daily life and work. To address this challenge, an intelligent combinational model is proposed based on improved empirical mode decomposition and support vector machine for short-term forecasting of ice cover thickness. Firstly, in light of the characteristics of ice cover thickness data, fast independent component analysis (FICA) is implemented to smooth the abnormal situation on the curve trend of the original data for prediction. Secondly, ensemble empirical mode decomposition (EEMD) decomposes data after denoising it into different components from high frequency to low frequency, and support vector machine (SVM) is introduced to predict the sequence of different components. Then, some modifications are performed on the standard SVM algorithm to accelerate the convergence speed. Combined with the advantages of genetic algorithm and tabu search, the combination algorithm is introduced to optimize the parameters of support vector machine. To improve the prediction accuracy, the kernel function of the support vector machine is adaptively adopted according to the complexity of different sequences. Finally, prediction results for each component series are added to obtain the overall ice cover thickness. A 220 kV DC transmission line in the Hunan Region is taken as the case study to verify the practicability and effectiveness of the proposed method. Meanwhile, we select SVM optimized by genetic algorithm (GA-SVM) and traditional SVM algorithm for comparison, and use the error function of mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) to compare prediction accuracy. Finally, we find that these improvements facilitate the forecasting efficiency and improve the performance of the model. As a result, the proposed model obtains more ideal solutions and has higher accuracy and stronger generalization than other algorithms.

ACS Style

Xiaomin Xu; Dongxiao Niu; Lihui Zhang; Yongli Wang; Keke Wang. Ice Cover Prediction of a Power Grid Transmission Line Based on Two-Stage Data Processing and Adaptive Support Vector Machine Optimized by Genetic Tabu Search. Energies 2017, 10, 1862 .

AMA Style

Xiaomin Xu, Dongxiao Niu, Lihui Zhang, Yongli Wang, Keke Wang. Ice Cover Prediction of a Power Grid Transmission Line Based on Two-Stage Data Processing and Adaptive Support Vector Machine Optimized by Genetic Tabu Search. Energies. 2017; 10 (11):1862.

Chicago/Turabian Style

Xiaomin Xu; Dongxiao Niu; Lihui Zhang; Yongli Wang; Keke Wang. 2017. "Ice Cover Prediction of a Power Grid Transmission Line Based on Two-Stage Data Processing and Adaptive Support Vector Machine Optimized by Genetic Tabu Search." Energies 10, no. 11: 1862.

Journal article
Published: 07 July 2016 in Sustainability
Reads 0
Downloads 0

With the rapid development of renewable energy, power supply structure is changing. However, thermal power is still dominant. With the background in low carbon economy, reasonable adjustment and optimization of the power supply structure is the trend of future development in the power industry. It is also a reliable guarantee of a fast, healthy and stable development of national economy. In this paper, the sustainable development of renewable energy sources is analyzed from the perspective of power supply. Through the research on the development of power supply structure, we find that regional power supply structure development mode conforms to dynamic characteristics and there must exist a Markov chain in the final equilibrium state. Combined with the characteristics of no aftereffect and small samples, this paper applies a Markov model to the power supply structure prediction. The optimization model is established to ensure that the model can fit the historical data as much as possible. Taking actual data of a certain area of Ningxia Province as an example, the models proposed in this paper are applied to the practice and results verify the validity and robustness of the model, which can provide decision basis for enterprise managers.

ACS Style

Xiaomin Xu; Dongxiao Niu; Jinpeng Qiu; Peng Wang; Yanchao Chen. Analysis and Optimization of Power Supply Structure Based on Markov Chain and Error Optimization for Renewable Energy from the Perspective of Sustainability. Sustainability 2016, 8, 634 .

AMA Style

Xiaomin Xu, Dongxiao Niu, Jinpeng Qiu, Peng Wang, Yanchao Chen. Analysis and Optimization of Power Supply Structure Based on Markov Chain and Error Optimization for Renewable Energy from the Perspective of Sustainability. Sustainability. 2016; 8 (7):634.

Chicago/Turabian Style

Xiaomin Xu; Dongxiao Niu; Jinpeng Qiu; Peng Wang; Yanchao Chen. 2016. "Analysis and Optimization of Power Supply Structure Based on Markov Chain and Error Optimization for Renewable Energy from the Perspective of Sustainability." Sustainability 8, no. 7: 634.

Research article
Published: 07 April 2015 in Mathematical Problems in Engineering
Reads 0
Downloads 0

Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the peoples daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR). According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO), which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.

ACS Style

Xiaomin Xu; Dongxiao Niu; Peng Wang; Yan Lu; Huicong Xia. The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line. Mathematical Problems in Engineering 2015, 2015, 1 -9.

AMA Style

Xiaomin Xu, Dongxiao Niu, Peng Wang, Yan Lu, Huicong Xia. The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line. Mathematical Problems in Engineering. 2015; 2015 (4):1-9.

Chicago/Turabian Style

Xiaomin Xu; Dongxiao Niu; Peng Wang; Yan Lu; Huicong Xia. 2015. "The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line." Mathematical Problems in Engineering 2015, no. 4: 1-9.