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Dr. Yi Liang is at the School of Management, Hebei GEO University, China. His research interests mainly include low carbon energy management, intelligent forecasting technology. Dr. Liang’s articles have been published in Energy, Journal of Cleaner Production, Energies, Sustainability and Journal of Energy Engineering among others.
With the change in energy utilization, a fast and accurate evaluation method is of great importance to promote green campus sustainability. In order to improve the feasibility and timeliness of evaluation, an intelligent evaluation model based on dynamic Bayesian inference and adaptive network fuzzy inference system (DBN-ANFIS) is proposed. Firstly, from the perspective of sustainability and considering the changes in energy utilization, a green campus evaluation index system is constructed from four levels: campus resource utilization, campus environment creation, campus usage management, and campus eco-efficiency. On this basis, the parameters of the adaptive network fuzzy inference system (ANFIS) are optimized based on dynamic Bayesian inference (DBN), so as to apply the modified model to the green campus evaluation work of the Spark big data operation platform. Finally, the scientificity of the model proposed in this paper is verified through example analysis, which is conducive to the real-time and effective evaluation of green campus sustainability and provides scientific and rational decision support to improve its management.
Hongmei Zhao; Yang Xu; Wei-Chiang Hong; Yi Liang; Dandan Zou. Smart Evaluation of Green Campus Sustainability Considering Energy Utilization. Sustainability 2021, 13, 7653 .
AMA StyleHongmei Zhao, Yang Xu, Wei-Chiang Hong, Yi Liang, Dandan Zou. Smart Evaluation of Green Campus Sustainability Considering Energy Utilization. Sustainability. 2021; 13 (14):7653.
Chicago/Turabian StyleHongmei Zhao; Yang Xu; Wei-Chiang Hong; Yi Liang; Dandan Zou. 2021. "Smart Evaluation of Green Campus Sustainability Considering Energy Utilization." Sustainability 13, no. 14: 7653.
Scientific and timely sustainability evaluation of the photovoltaic industry along the Belt and Road is of great significance. In this paper, a novel hybrid evaluation model is proposed for accurate and real-time assessment that integrates modified set pair analysis with least squares support vector machine that combines improved cuckoo search algorithm. First, the indicator system is set from five principles, namely economy, politics, society, ecological environment and resources. Then, the traditional approach is established through modifying set pair analysis on the basis of variable fuzzy set coupling evaluation theory. A modern intelligent assessment model is designed that integrates improved cuckoo search algorithm and least squares support vector machine where the concept of random weight is introduced in improved cuckoo search algorithm. In the case analysis, the relative errors calculated by the proposed model all fluctuate in the range of [−3%, 3%], indicating that it has the strongest fitting and learning ability. The empirical analysis verifies the scientificity and precision of the method and points out influencing factors. It provides a new idea for rapid and effective assessment of PV industry along the Belt and Road, as well as assistance for the sustainable development of this industry. This paper innovatively proposes the sustainability evaluation index system and evaluation model for the photovoltaic industry in countries along the Belt and Road, thus contributing to the promotion of sustainable development of the photovoltaic industry in countries along the Belt and Road.
Yi Liang; Haichao Wang. Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road. Energies 2021, 14, 3420 .
AMA StyleYi Liang, Haichao Wang. Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road. Energies. 2021; 14 (12):3420.
Chicago/Turabian StyleYi Liang; Haichao Wang. 2021. "Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road." Energies 14, no. 12: 3420.
The research on the sustainability evaluation of innovation and entrepreneurship education for clean energy majors in colleges and universities can not only cultivate more and better innovative and entrepreneurial talents for the development of sustainable energy but also provide a reference for the sustainable development of innovation and entrepreneurship education for other majors. To achieve systematic and comprehensive scientific evaluation, this paper proposes an evaluation model based on SPA-VFS and Chaos bat algorithm to optimize GRNN. Firstly, the sustainability evaluation index system of innovation and entrepreneurship education for clean energy major in colleges and universities is constructed from the four aspects of the environment, investment, process, and results, and the meaning of each evaluation index is explained; Then, combined with variable fuzzy set evaluation theory (VFS) and set pair analysis theory (SPA), the classical evaluation model based on SPA-VFS is constructed, and the entropy weight method and rank method are coupled to obtain the index weight. The basic bat algorithm is improved by using Tent chaotic mapping, and the chaotic bat algorithm (CBA) is proposed. The generalized regression neural network (GRNN) model is optimized by CBA, and the intelligent evaluation model based on CBA-GRNN is obtained to realize fast real-time calculation; finally, a numerical example is used to verify the scientificity and accuracy of the model proposed in this paper. This study is conducive to a comprehensive evaluation of the sustainability of innovation and entrepreneurship education for clean energy major in colleges and universities, and is conducive to the healthy and sustainable development of innovation and entrepreneurship education for clean energy major in colleges and universities, so as to provide more innovative and entrepreneurial talents for the clean energy industry.
Yi Liang; Haichao Wang; Wei-Chiang Hong. Sustainable Development Evaluation of Innovation and Entrepreneurship Education of Clean Energy Major in Colleges and Universities Based on SPA-VFS and GRNN Optimized by Chaos Bat Algorithm. Sustainability 2021, 13, 5960 .
AMA StyleYi Liang, Haichao Wang, Wei-Chiang Hong. Sustainable Development Evaluation of Innovation and Entrepreneurship Education of Clean Energy Major in Colleges and Universities Based on SPA-VFS and GRNN Optimized by Chaos Bat Algorithm. Sustainability. 2021; 13 (11):5960.
Chicago/Turabian StyleYi Liang; Haichao Wang; Wei-Chiang Hong. 2021. "Sustainable Development Evaluation of Innovation and Entrepreneurship Education of Clean Energy Major in Colleges and Universities Based on SPA-VFS and GRNN Optimized by Chaos Bat Algorithm." Sustainability 13, no. 11: 5960.
With the development of renewable energy, the grid connection is faced with great pressure, for its generation uncertainty and fluctuation requires larger reserve capacity, and higher operation costs. Energy storage system, as a flexible unit in the energy system, can effectively share the reserve pressure of the system by charging and discharging behaviors. In order to further improve the renewable energy utilization, the combination of wind power and energy storage for hybrid energy system is proposed. On considering the power generation characteristics, the objective functions are maximizing the system revenue and minimizing the system energy loss. Combined with the robust optimization theory, the model is transformed and solved. The results show that the application of the energy storage will effectively promote the renewable energy consumption, and the combination of the wind power and energy storage will achieve more effective utilization of the night-time wind power and cut down the total system cost.
Jing Wu; Zhongfu Tan; Keke Wang; Yi Liang; Jinghan Zhou. Research on Multi-Objective Optimization Model for Hybrid Energy System Considering Combination of Wind Power and Energy Storage. Sustainability 2021, 13, 3098 .
AMA StyleJing Wu, Zhongfu Tan, Keke Wang, Yi Liang, Jinghan Zhou. Research on Multi-Objective Optimization Model for Hybrid Energy System Considering Combination of Wind Power and Energy Storage. Sustainability. 2021; 13 (6):3098.
Chicago/Turabian StyleJing Wu; Zhongfu Tan; Keke Wang; Yi Liang; Jinghan Zhou. 2021. "Research on Multi-Objective Optimization Model for Hybrid Energy System Considering Combination of Wind Power and Energy Storage." Sustainability 13, no. 6: 3098.
With the development of renewable energy, renewable energy incubators have emerged continuously. However, these incubators present a crude development model of low-level replication and large-scale expansion, which has triggered a series of urgent problems including unbalanced regional development, low incubation efficiency, low resource utilization, and vicious competition for resources. There are huge challenges for the sustainable development of incubators in the future. A scientific and accurate evaluation approach is of great significance for improving the sustainability of renewable energy incubators. Therefore, this paper proposes a novel method combining an interval type-II fuzzy analytic hierarchy process (AHP) with mind evolutionary algorithm-modified least-squares support vector machine (MEA-MLSSVM). The indicator system is established from two aspects: service capability and operational efficiency. TOPSIS integrated with an interval type-II fuzzy AHP is employed for index weighting and assessment. In the least-squares support vector machine (LSSVM), the traditional radial basis function is replaced with the wavelet transform function (WT), and the parameters are fine-tuned by the mind evolutionary algorithm (MEA). Accordingly, the establishment of a comprehensive sustainability evaluation model for renewable energy incubators is accomplished in this paper. The experimental study reveals that this novel technique has the advantages of scientificity and precision and provides a decision-making basis for renewable energy incubators to realize sustainable operation.
Guangqi Liang; Dongxiao Niu; Yi Liang. Sustainability Evaluation of Renewable Energy Incubators Using Interval Type-II Fuzzy AHP-TOPSIS with MEA-MLSSVM. Sustainability 2021, 13, 1796 .
AMA StyleGuangqi Liang, Dongxiao Niu, Yi Liang. Sustainability Evaluation of Renewable Energy Incubators Using Interval Type-II Fuzzy AHP-TOPSIS with MEA-MLSSVM. Sustainability. 2021; 13 (4):1796.
Chicago/Turabian StyleGuangqi Liang; Dongxiao Niu; Yi Liang. 2021. "Sustainability Evaluation of Renewable Energy Incubators Using Interval Type-II Fuzzy AHP-TOPSIS with MEA-MLSSVM." Sustainability 13, no. 4: 1796.
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.
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 StyleKeke 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 StyleKeke 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.
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.
Haichao Wang; Yi Liang; Wei Ding; Dongxiao Niu; Si Li; Fenghua Wang. The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects. Mathematical Problems in Engineering 2020, 2020, 1 -14.
AMA StyleHaichao Wang, Yi Liang, Wei Ding, Dongxiao Niu, Si Li, Fenghua Wang. The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects. Mathematical Problems in Engineering. 2020; 2020 ():1-14.
Chicago/Turabian StyleHaichao Wang; Yi Liang; Wei Ding; Dongxiao Niu; Si Li; Fenghua Wang. 2020. "The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects." Mathematical Problems in Engineering 2020, no. : 1-14.
Scientific and accurate core competitiveness evaluation of clean energy incubators is of great significance for improving their burgeoning development. Hence, this paper proposes a hybrid model on the basis of matter-element extension integrated with TOPSIS and KPCA-NSGA-II-LSSVM. The core competitiveness evaluation index system of clean energy incubators is established from five aspects, namely strategic positioning ability, seed selection ability, intelligent transplantation ability, growth catalytic ability and service value-added ability. Then matter-element extension and TOPSIS based on entropy weight is applied to index weighting and comprehensive evaluation. For the purpose of feature dimension reduction, kernel principal component analysis (KPCA) is used to extract momentous information among variables as the input. The evaluation results can be obtained by least squares support vector machine (LSSVM) optimized by NSGA-II. The experiment study validates the precision and applicability of this novel approach, which is conducive to comprehensive evaluation of the core competitiveness for clean energy incubators and decision-making for more reasonable operation.
Guangqi Liang; Dongxiao Niu; Yi Liang. Core Competitiveness Evaluation of Clean Energy Incubators Based on Matter-Element Extension Combined with TOPSIS and KPCA-NSGA-II-LSSVM. Sustainability 2020, 12, 9570 .
AMA StyleGuangqi Liang, Dongxiao Niu, Yi Liang. Core Competitiveness Evaluation of Clean Energy Incubators Based on Matter-Element Extension Combined with TOPSIS and KPCA-NSGA-II-LSSVM. Sustainability. 2020; 12 (22):9570.
Chicago/Turabian StyleGuangqi Liang; Dongxiao Niu; Yi Liang. 2020. "Core Competitiveness Evaluation of Clean Energy Incubators Based on Matter-Element Extension Combined with TOPSIS and KPCA-NSGA-II-LSSVM." Sustainability 12, no. 22: 9570.
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.
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 StyleHao 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 StyleHao 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.
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.
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 StyleDongxiao 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 StyleDongxiao 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.
Along with the deregulation of electric power market as well as aggregation of renewable resources, short term load forecasting (STLF) has become more and more momentous. However, it is a hard task due to various influential factors that leads to volatility and instability of the series. Therefore, this paper proposes a hybrid model which combines empirical mode decomposition (EMD), minimal redundancy maximal relevance (mRMR), general regression neural network (GRNN) with fruit fly optimization algorithm (FOA), namely EMD-mRMR-FOA-GRNN. The original load series is firstly decomposed into a quantity of intrinsic mode functions (IMFs) and a residue with different frequency so as to weaken the volatility of the series influenced by complicated factors. Then, mRMR is employed to obtain the best feature set through the correlation analysis between each IMF and the features including day types, temperature, meteorology conditions and so on. Finally, FOA is utilized to optimize the smoothing factor in GRNN. The ultimate forecasted load can be derived from the summation of the predicted results for all IMFs. To validate the proposed technique, load data in Langfang, China are provided. The results demonstrate that EMD-mRMR-FOA-GRNN is a promising approach in terms of STLF.
Yi Liang; Dongxiao Niu; Wei-Chiang Hong. Short term load forecasting based on feature extraction and improved general regression neural network model. Energy 2018, 166, 653 -663.
AMA StyleYi Liang, Dongxiao Niu, Wei-Chiang Hong. Short term load forecasting based on feature extraction and improved general regression neural network model. Energy. 2018; 166 ():653-663.
Chicago/Turabian StyleYi Liang; Dongxiao Niu; Wei-Chiang Hong. 2018. "Short term load forecasting based on feature extraction and improved general regression neural network model." Energy 166, no. : 653-663.
The Beijing-Tianjin-Hebei (B-T-H) region, who captures the national strategic highland in China, has drawn a great deal of attention due to the fog and haze condition and other environmental problems. Further, the high carbon emissions generated by energy consumption has restricted its further coordinated development seriously. In order to accurately analyze the potential influencing factors that contribute to the growth of energy consumption carbon emissions in the B-T-H region, this paper uses the carbon emission coefficient method to measure the carbon emissions of energy consumption in the B-T-H region, using a weighted combination based on Logarithmic Mean Divisia Index (LMDI) and Shapley Value (SV). The effects affecting carbon emissions during 2001–2013 caused from five aspects, including energy consumption structure, energy consumption intensity, industrial structure, economic development and population size, are quantitatively analyzed. The results indicated that: (1) The carbon emissions had shown a sustained growth trend in the B-T-H region on the whole, while the growth rates varied in the three areas. In detail, Hebei Province got the first place in carbon emissions growth, followed by Tianjin and Beijing; (2) economic development was the main driving force for the carbon emissions growth of energy consumption in B-T-H region. Energy consumption structure, population size and industrial structure promoted carbon emissions growth as well, but their effects weakened in turn and were less obvious than that of economic development; (3) energy consumption intensity had played a significant inhibitory role on the carbon emissions growth; (4) it was of great significance to ease the carbon emission-reduction pressure of the B-T-H region from the four aspects of upgrading industrial structure adjustment, making technological progress, optimizing the energy structure and building long-term carbon-emission-reduction mechanisms, so as to promote the coordinated low-carbon development.
Yi Liang; Dongxiao Niu; Weiwei Zhou; Yingying Fan. Decomposition Analysis of Carbon Emissions from Energy Consumption in Beijing-Tianjin-Hebei, China: A Weighted-Combination Model Based on Logarithmic Mean Divisia Index and Shapley Value. Sustainability 2018, 10, 2535 .
AMA StyleYi Liang, Dongxiao Niu, Weiwei Zhou, Yingying Fan. Decomposition Analysis of Carbon Emissions from Energy Consumption in Beijing-Tianjin-Hebei, China: A Weighted-Combination Model Based on Logarithmic Mean Divisia Index and Shapley Value. Sustainability. 2018; 10 (7):2535.
Chicago/Turabian StyleYi Liang; Dongxiao Niu; Weiwei Zhou; Yingying Fan. 2018. "Decomposition Analysis of Carbon Emissions from Energy Consumption in Beijing-Tianjin-Hebei, China: A Weighted-Combination Model Based on Logarithmic Mean Divisia Index and Shapley Value." Sustainability 10, no. 7: 2535.
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.
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 StyleHaichao 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 StyleHaichao 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.
In the context of low-carbon economy, using environment-friendly and energy-friendly energies is an important measure to achieve sustainable development. In view of the various forms of energy supply, in addition to the most basic economic factors, the paper introduces environmental and social impact , and together constitute the comprehensive benefit...
Guorong Zhu; Keke Wang; Yi Liang; Dongxiao Niu. Comprehensive Benefit Evaluation Model and Analysis Considering Multi - Energy Supply. Proceedings of the 2017 International Conference Advanced Engineering and Technology Research (AETR 2017) 2018, 1 .
AMA StyleGuorong Zhu, Keke Wang, Yi Liang, Dongxiao Niu. Comprehensive Benefit Evaluation Model and Analysis Considering Multi - Energy Supply. Proceedings of the 2017 International Conference Advanced Engineering and Technology Research (AETR 2017). 2018; ():1.
Chicago/Turabian StyleGuorong Zhu; Keke Wang; Yi Liang; Dongxiao Niu. 2018. "Comprehensive Benefit Evaluation Model and Analysis Considering Multi - Energy Supply." Proceedings of the 2017 International Conference Advanced Engineering and Technology Research (AETR 2017) , no. : 1.
Interruptible load management utilizes user flexibility to relieve power shortage during peak load, which can avoid or reduce the investment of high power spinning reserve and meet the increase of electricity demand. There are lots of uncertain risk factors in the process of interruptible load management, so we must consider the uncertain risk of user...
Fan Wen; Ze Sun; Yi Liang; Dongxiao Niu. Uncertainty Risk Assessment of User - side Flexible Resource Scheduling Based on Entropy Weight - TOPSIS Method. Proceedings of the 2017 International Conference Advanced Engineering and Technology Research (AETR 2017) 2018, 1 .
AMA StyleFan Wen, Ze Sun, Yi Liang, Dongxiao Niu. Uncertainty Risk Assessment of User - side Flexible Resource Scheduling Based on Entropy Weight - TOPSIS Method. Proceedings of the 2017 International Conference Advanced Engineering and Technology Research (AETR 2017). 2018; ():1.
Chicago/Turabian StyleFan Wen; Ze Sun; Yi Liang; Dongxiao Niu. 2018. "Uncertainty Risk Assessment of User - side Flexible Resource Scheduling Based on Entropy Weight - TOPSIS Method." Proceedings of the 2017 International Conference Advanced Engineering and Technology Research (AETR 2017) , no. : 1.
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.
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 StyleDongxiao 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 StyleDongxiao 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.
As a kind of clean and renewable energy, wind power is winning more and more attention across the world. Regarding wind power utilization, safety is a core concern and such concern has led to many studies on predicting wind speed. To obtain a more accurate prediction of the wind speed, this paper adopts a new hybrid forecasting model, combing empirical mode decomposition (EMD) and the general regression neural network (GRNN) optimized by the fruit fly optimization algorithm (FOA). In this new model, the original wind speed series are first decomposed into a collection of intrinsic mode functions (IMFs) and a residue. Next, the inherent relationship (partial correlation) of the datasets is analyzed, and the results are then used to select the input for the forecasting model. Finally, the GRNN with the FOA to optimize the smoothing factor is used to predict each sub-series. The mean absolute percentage error of the forecasting results in two cases are respectively 8.95% and 9.87%, suggesting that the hybrid approach outperforms the compared models, which provides guidance for future wind speed forecasting.
Dongxiao Niu; Yi Liang; Wei-Chiang Hong. Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA. Energies 2017, 10, 2001 .
AMA StyleDongxiao Niu, Yi Liang, Wei-Chiang Hong. Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA. Energies. 2017; 10 (12):2001.
Chicago/Turabian StyleDongxiao Niu; Yi Liang; Wei-Chiang Hong. 2017. "Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA." Energies 10, no. 12: 2001.
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.
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 StyleYi 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 StyleYi 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.
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.
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 StyleDongxiao 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 StyleDongxiao 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.
Against the backdrop of increasingly serious global climate change and the development of the low-carbon economy, the coordination between energy consumption carbon emissions (ECCE) and regional population, resources, environment, economy and society has become an important subject. In this paper, the research focuses on the security early warning of ECCE in Hebei Province, China. First, an assessment index system of the security early warning of ECCE is constructed based on the pressure-state-response (P-S-R) model. Then, the variance method and linearity weighted method are used to calculate the security early warning index of ECCE. From the two dimensions of time series and spatial pattern, the security early warning conditions of ECCE are analyzed in depth. Finally, with the assessment analysis of the data from 2000 to 2014, the prediction of the security early warning of carbon emissions from 2015 to 2020 is given, using a back propagation neural network based on a kidney-inspired algorithm (KA-BPNN) model. The results indicate that: (1) from 2000 to 2014, the security comprehensive index of ECCE demonstrates a fluctuating upward trend in general and the trend of the alarm level is “Severe warning”–“Moderate warning”–“Slight warning”; (2) there is a big spatial difference in the security of ECCE, with relatively high-security alarm level in the north while it is relatively low in the other areas; (3) the security index shows the trend of continuing improvement from 2015 to 2020, however the security level will remain in the state of “Semi-secure” for a long time and the corresponding alarm is still in the state of “Slight warning”, reflecting that the situation is still not optimistic.
Yi Liang; Dongxiao Niu; Haichao Wang; Hanyu Chen. Assessment Analysis and Forecasting for Security Early Warning of Energy Consumption Carbon Emissions in Hebei Province, China. Energies 2017, 10, 391 .
AMA StyleYi Liang, Dongxiao Niu, Haichao Wang, Hanyu Chen. Assessment Analysis and Forecasting for Security Early Warning of Energy Consumption Carbon Emissions in Hebei Province, China. Energies. 2017; 10 (3):391.
Chicago/Turabian StyleYi Liang; Dongxiao Niu; Haichao Wang; Hanyu Chen. 2017. "Assessment Analysis and Forecasting for Security Early Warning of Energy Consumption Carbon Emissions in Hebei Province, China." Energies 10, no. 3: 391.