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< Page ,Total 28 >
Quantitative Analysis of the Impact of Power Market and Policy on Renewable Energy Utilization EI Scopus
会议论文 | 2022 , 976-981 | 5th International Conference on Energy, Electrical and Power Engineering, CEEPE 2022
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Abstract :

Promoting the construction of the power market and the implementation of renewable energy policies to facilitate the use of renewable energy has become the industry consensus. In recent years, the renewable energy installed capacity in China has exploded, the varieties of market-oriented transactions for renewable energy have been continuously enriched, and the renewable energy policies also have been improved. However, the lack of quantitative analysis synthetically considering the impact of market, policy, and physical boundary on renewable energy utilization makes it difficult to measure and compare the effect of power market and policies. To solve this problem, this paper constructs a quantitative analysis model for renewable energy utilization and analyzes the influence mechanism of the market, policy, and physical boundary on renewable energy utilization to minimize system operating costs. The validity of the proposed model is verified by numerical results. © 2022 IEEE.

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GB/T 7714 Xu, Liang , Dong, Xiaoliang , Qiao, Ning et al. Quantitative Analysis of the Impact of Power Market and Policy on Renewable Energy Utilization [C] . 2022 : 976-981 .
MLA Xu, Liang et al. "Quantitative Analysis of the Impact of Power Market and Policy on Renewable Energy Utilization" . (2022) : 976-981 .
APA Xu, Liang , Dong, Xiaoliang , Qiao, Ning , Zhang, Chao , Sun, Yuge , Ding, Tao . Quantitative Analysis of the Impact of Power Market and Policy on Renewable Energy Utilization . (2022) : 976-981 .
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Multi-Period Restoration Model for Integrated Power-Hydrogen Systems Considering Transportation States EI SCIE Scopus
期刊论文 | 2022 , 58 (2) , 2694-2706 | IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
WoS CC Cited Count: 4 SCOPUS Cited Count: 8
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Abstract :

This article proposes an innovative integrated power and hydrogen distribution system (IPHDS) restoration model in response to multiple outages caused by natural disasters. During the restoration, repair crews and mobile battery-carried vehicles are considered to repair faulted lines and support critical power loads. Also, the network reconfiguration is taken into consideration in the restoration model to pick up loads. Besides, to address the different response time of hydrogen and power systems, the aerodynamic law-based dynamic hydrogen flow model is applied in the hydrogen system. The proposed model is presented as a mixed-integer linear program, which is verified on a 33-bus-48-node IPHDS with multiple outages. The present results verify the effectiveness of the proposed method.

Keyword :

Aerodynamics Dynamic hydrogen flow Hydrogen integrated system restoration Load modeling Maintenance engineering mobile battery-carried vehicles (MBCVs) Natural gas network reconfiguration Power system dynamics repair crews (RCs) resilience Resilience

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GB/T 7714 Wang, Zekai , Ding, Tao , Jia, Wenhao et al. Multi-Period Restoration Model for Integrated Power-Hydrogen Systems Considering Transportation States [J]. | IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS , 2022 , 58 (2) : 2694-2706 .
MLA Wang, Zekai et al. "Multi-Period Restoration Model for Integrated Power-Hydrogen Systems Considering Transportation States" . | IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 58 . 2 (2022) : 2694-2706 .
APA Wang, Zekai , Ding, Tao , Jia, Wenhao , Mu, Chenggang , Huang, Can , Catalao, Joao P. S. . Multi-Period Restoration Model for Integrated Power-Hydrogen Systems Considering Transportation States . | IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS , 2022 , 58 (2) , 2694-2706 .
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Power System Frequency Regulation Model Based on Blockchain Community Thinking EI Scopus
期刊论文 | 2022 , 42 (4) , 1375-1387 | Proceedings of the Chinese Society of Electrical Engineering
SCOPUS Cited Count: 1
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Abstract :

Frequency regulation (FR) plays an important role in maintaining the power balance and stability of power systems. How to fairly and reasonably compensate generators and actively stimulate generators in the frequency regulation ancillary service market is an urgent problem to be solved by power market reforms. In this paper, a blockchain community thinking with decentralization, multi-party consensus and token incentive was proposed. Based on the token incentive feature, an FR credit was designed to integrate the global FR effect and individual participation performance, and then a frequency response model of the distributed FR system with decentralized features was established. Furthermore, the settlement process of the FR credit was designed by using multiparty consensus thinking, which can motivate generators to participate in FR and ensure the authenticity and reliability of the results. Case study verifies the effectiveness of the distributed FR system, which can effectively complete the system FR tasks and credit settlements, and fully mobilize the FR generators to enhance the efficiency of distributed FR resources. © 2022 Chin. Soc. for Elec. Eng.

Keyword :

Blockchain Commerce Frequency response

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GB/T 7714 Mu, Chenggang , Ding, Tao , Ju, Chang et al. Power System Frequency Regulation Model Based on Blockchain Community Thinking [J]. | Proceedings of the Chinese Society of Electrical Engineering , 2022 , 42 (4) : 1375-1387 .
MLA Mu, Chenggang et al. "Power System Frequency Regulation Model Based on Blockchain Community Thinking" . | Proceedings of the Chinese Society of Electrical Engineering 42 . 4 (2022) : 1375-1387 .
APA Mu, Chenggang , Ding, Tao , Ju, Chang , Li, Li , Chi, Fangde , He, Yuankang et al. Power System Frequency Regulation Model Based on Blockchain Community Thinking . | Proceedings of the Chinese Society of Electrical Engineering , 2022 , 42 (4) , 1375-1387 .
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A Two-Layer Model for Microgrid Real-Time Scheduling Using Approximate Future Cost Function EI SCIE Scopus
期刊论文 | 2022 , 37 (2) , 1264-1273 | IEEE TRANSACTIONS ON POWER SYSTEMS
WoS CC Cited Count: 3 SCOPUS Cited Count: 15
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Abstract :

Microgrids incorporate an increasing number of distributed energy resources (DERs), which induce a higher variability and faster dispatch capabilities in power systems. This paper proposes a two-layer real-time scheduling model for microgrids, based on approximate future cost function (AFCF), where the future cost represents the opportunity cost for the microgrid operation in subsequent periods. At the upper layer, the look-ahead rolling scheduling is adopted to optimize microgrid operations, in which the future cost function (FCF) in deterministic and stochastic scenarios is approximated by a piecewise linear function. At the lower layer, a real-time parameter updating strategy based on real-time data is proposed. In this case, the real-time scheduling readjusts the look-ahead schedule using the immediate cost in the current period and the future cost calculated by the updated AFCF. The proposed two-layer real-time scheduling model uses an offline optimization, in which most of the computation tasks are completed at the upper layer, and applies a real-time optimization, in which the time-consuming problem is avoided at the lower layer. The effectiveness of the proposed two-layer real-time scheduling model of microgrids is validated by using a grid-connected microgrid system. For comparison, other existing real-time scheduling methods are also implemented in the same microgrid system.

Keyword :

Cost function Dynamic scheduling future cost function Load modeling Microgrid Microgrids Processor scheduling real-time scheduling Real-time systems State of charge two-layer scheduling

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GB/T 7714 Liu, Chunyang , Zhang, Hengxu , Shahidehpour, Mohammad et al. A Two-Layer Model for Microgrid Real-Time Scheduling Using Approximate Future Cost Function [J]. | IEEE TRANSACTIONS ON POWER SYSTEMS , 2022 , 37 (2) : 1264-1273 .
MLA Liu, Chunyang et al. "A Two-Layer Model for Microgrid Real-Time Scheduling Using Approximate Future Cost Function" . | IEEE TRANSACTIONS ON POWER SYSTEMS 37 . 2 (2022) : 1264-1273 .
APA Liu, Chunyang , Zhang, Hengxu , Shahidehpour, Mohammad , Zhou, Quan , Ding, Tao . A Two-Layer Model for Microgrid Real-Time Scheduling Using Approximate Future Cost Function . | IEEE TRANSACTIONS ON POWER SYSTEMS , 2022 , 37 (2) , 1264-1273 .
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Pathways of clean energy heating electrification programs for reducing carbon emissions in Northwest China EI SCIE Scopus
期刊论文 | 2022 , 166 | Renewable and Sustainable Energy Reviews
SCOPUS Cited Count: 8
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Abstract :

Clean energy heating electrification programs provide a promising way to reduce carbon emissions from fossil fuel combustion and consumption. This work studies the cost competitiveness of clean energy heating technologies under three dynamic mechanisms: investment costs, subsidy policies, and operating costs with real data. It provides key insights into the cost competitiveness of the different heating technologies deployed in different areas, as well as their sensitivity to the three dynamic mechanisms. The results show that currently, the distinct heating programs are more cost-efficient in the urban area with existing heating networks. The average payback period of all district clean energy heating programs in the urban area is 14.9 years, while that of the individual clean heating programs is 24.7 years. The individual heating programs are becoming increasingly cost-competitive with the incentive mechanisms, especially the electricity pricing mechanisms. Moreover, individual heating technologies present remarkable advantages on flexibility and sustainability in the long run. According to the technology diffusion model proposed in this paper, the individual clean heating programs will occupy more than 50% of the market share in 2050 under the comprehensive effect of CAPEX, government subsidies, and OPEX. The real-world results and analysis render references to shape the pathway of clean energy heating electrification in Northwest China and other regions with a similar situation. © 2022 Elsevier Ltd

Keyword :

Carbon Competition Cost benefit analysis Dynamics Electric utilities Fossil fuels Investments Operating costs Sustainable development

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GB/T 7714 Ding, Tao , Sun, Yuge , Huang, Can et al. Pathways of clean energy heating electrification programs for reducing carbon emissions in Northwest China [J]. | Renewable and Sustainable Energy Reviews , 2022 , 166 .
MLA Ding, Tao et al. "Pathways of clean energy heating electrification programs for reducing carbon emissions in Northwest China" . | Renewable and Sustainable Energy Reviews 166 (2022) .
APA Ding, Tao , Sun, Yuge , Huang, Can , Mu, Chenlu , Fan, Yuqi , Lin, Jiang et al. Pathways of clean energy heating electrification programs for reducing carbon emissions in Northwest China . | Renewable and Sustainable Energy Reviews , 2022 , 166 .
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Interval Total Transfer Capability for Mesh HVDC Systems Based on Sum of Squares and Multi-Dimensional Holomorphic Embedding Method EI SCIE Scopus
期刊论文 | 2022 , 37 (6) , 4157-4167 | IEEE TRANSACTIONS ON POWER SYSTEMS
SCOPUS Cited Count: 4
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Abstract :

Total transfer capability evaluation is an effective method to analyze the backbone transmission capability among regions. To address renewable energy uncertainties, an interval total transfer capability model based on the multi-dimensional holomorphic embedding method and sum of squares relaxation technique is proposed to solve the regional total transfer capability in meshed high voltage direct current systems. First, the multi-dimensional holomorphic embedding method is used to derive the analytical expressions of regional tie lines. Second, the interval total transfer capability model can be reformulated by two bi-level optimization models. Third, sum of squares relaxation is employed to solve the two optimization problems. Numerical results on a 40-bus European Synthetic System and the Chinese meshed high voltage direct current system validate the effectiveness of the proposed model and method.

Keyword :

high voltage direct current HVDC transmission Indexes Load flow Mathematical models Multi-dimensional holomorphic embedding method Renewable energy sources Security sum of squares relaxation Total transfer capability Uncertainty

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GB/T 7714 Sun, Yuge , Ding, Tao , Qu, Ming et al. Interval Total Transfer Capability for Mesh HVDC Systems Based on Sum of Squares and Multi-Dimensional Holomorphic Embedding Method [J]. | IEEE TRANSACTIONS ON POWER SYSTEMS , 2022 , 37 (6) : 4157-4167 .
MLA Sun, Yuge et al. "Interval Total Transfer Capability for Mesh HVDC Systems Based on Sum of Squares and Multi-Dimensional Holomorphic Embedding Method" . | IEEE TRANSACTIONS ON POWER SYSTEMS 37 . 6 (2022) : 4157-4167 .
APA Sun, Yuge , Ding, Tao , Qu, Ming , Wang, Fengyu , Shahidehpour, Mohammad . Interval Total Transfer Capability for Mesh HVDC Systems Based on Sum of Squares and Multi-Dimensional Holomorphic Embedding Method . | IEEE TRANSACTIONS ON POWER SYSTEMS , 2022 , 37 (6) , 4157-4167 .
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Multi-stage stochastic programming for resilient integrated electricity and natural gas distribution systems against typhoon natural disaster attacks EI SCIE Scopus
期刊论文 | 2022 , 159 | RENEWABLE & SUSTAINABLE ENERGY REVIEWS
WoS CC Cited Count: 1 SCOPUS Cited Count: 21
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Abstract :

Recent energy systems have witnessed a large number of extreme weather events due to the global climate change, resulting in severe damages and economic losses. To address this problem, this paper proposes a multi-stage stochastic programming model for resilient integrated electricity and natural gas distribution systems under typhoon natural disasters. A three-stage modeling is employed to quantify the system resilience against the dynamic process of a typhoon, where the network reinforcement, network reconfiguration, and network repairing are coordinated. Moreover, the interaction between the power system and natural gas system during a typhoon attack is reflected in this model. To address the uncertainties introduced by the typhoon moving paths, non-anticipativtity constraints are adopted in the proposed multi-stage stochastic programming model. Case studies on a 33-bus-48-node and a 144-bus-85-node integrated electricity and natural gas distribution systems verify the effectiveness of the proposed method. The results of the case studies show that the current IEGDS is still fragile when facing natural disasters, and building a resilient IEGDS to recover from natural disasters rapidly is meaningful in recent vigorous trends of developing integrated energy systems. By applying the proposed model, the proposed model and strategies can enable the IEGDS to reduce the load curtailment by about 52%, and the total economic loss will be reduced by 54%.

Keyword :

distribution system Integrated electricity and natural gas Multi-stage stochastic programming Network reinforcement Resilience Typhoon natural disasters

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GB/T 7714 Wang, Zekai , Ding, Tao , Jia, Wenhao et al. Multi-stage stochastic programming for resilient integrated electricity and natural gas distribution systems against typhoon natural disaster attacks [J]. | RENEWABLE & SUSTAINABLE ENERGY REVIEWS , 2022 , 159 .
MLA Wang, Zekai et al. "Multi-stage stochastic programming for resilient integrated electricity and natural gas distribution systems against typhoon natural disaster attacks" . | RENEWABLE & SUSTAINABLE ENERGY REVIEWS 159 (2022) .
APA Wang, Zekai , Ding, Tao , Jia, Wenhao , Huang, Can , Mu, Chenggang , Qu, Ming et al. Multi-stage stochastic programming for resilient integrated electricity and natural gas distribution systems against typhoon natural disaster attacks . | RENEWABLE & SUSTAINABLE ENERGY REVIEWS , 2022 , 159 .
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Solar Power Prediction Based on Satellite Measurements - A Graphical Learning Method for Tracking Cloud Motion EI SCIE Scopus
期刊论文 | 2022 , 37 (3) , 2335-2345 | IEEE TRANSACTIONS ON POWER SYSTEMS
SCOPUS Cited Count: 24
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Abstract :

The stochastic cloud cover on photovoltaic (PV) panels affects the solar power outputs, producing high instability in the integrated power systems. It is an effective approach to track the cloud motion during short-term PV power forecasting based on data sources of satellite images. However, since temporal variations of these images are noisy and non-stationary, pixel-sensitive prediction methods are critically needed in order to seek a balance between the forecast precision and the huge computation burden due to a large image size. Hence, a graphical learning framework is proposed in this study for intra-hour PV power prediction. By simulating the cloud motion using bi-directional extrapolation, a directed graph is generated representing the pixel values from multiple frames of historical images. The nodes and edges in the graph denote the shapes and motion directions of the regions of interest (ROIs) in satellite images. A spatial-temporal graph neural network (GNN) is then proposed to deal with the graph. Comparing with conventional deep-learning-based models, GNN is more flexible for varying sizes of input, in order to be able to handle dynamic ROIs. Referring to the comparative studies, the proposed method greatly reduces the redundancy of image inputs without sacrificing the visual scope, and slightly improves the prediction accuracy.

Keyword :

Bidirectional control Brightness temperature Clouds deep learning Extrapolation Forecasting graphical learning Predictive models satellite images Satellites Solar PV power prediction

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GB/T 7714 Cheng, Lilin , Zang, Haixiang , Wei, Zhinong et al. Solar Power Prediction Based on Satellite Measurements - A Graphical Learning Method for Tracking Cloud Motion [J]. | IEEE TRANSACTIONS ON POWER SYSTEMS , 2022 , 37 (3) : 2335-2345 .
MLA Cheng, Lilin et al. "Solar Power Prediction Based on Satellite Measurements - A Graphical Learning Method for Tracking Cloud Motion" . | IEEE TRANSACTIONS ON POWER SYSTEMS 37 . 3 (2022) : 2335-2345 .
APA Cheng, Lilin , Zang, Haixiang , Wei, Zhinong , Ding, Tao , Sun, Guoqiang . Solar Power Prediction Based on Satellite Measurements - A Graphical Learning Method for Tracking Cloud Motion . | IEEE TRANSACTIONS ON POWER SYSTEMS , 2022 , 37 (3) , 2335-2345 .
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Online Rectangle Packing Algorithm for Swapped Battery Charging Dispatch Model Considering Continuous Charging Power SCIE Scopus
期刊论文 | 2022 | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
SCOPUS Cited Count: 2
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Abstract :

The vigorous development of electric vehicles (EVs) is an important means of reducing carbon emissions and mitigating environmental problems such as the greenhouse effect. Battery swapping stations (BSSs) can both provide battery swapping services for large-scale EVs and charge batteries centrally. As the supply of fully charged batteries in the BSS shrinks, it becomes necessary to schedule the charging of the depleted batteries rapidly that users have swapped for fully-charged ones. The charging schedule for depleted batteries must be made without knowledge of future battery arrivals. In this context, this paper develops a mathematical model for online charging scheduling of BSSs, formulates the charging strategy as a two-dimensional rectangle packing problem, and quickly calculates the scheduling arrangement of batteries by partitioning the remaining available capacity of a BSS. Since there are limited battery types within the BSS which can provide battery replacement services, this paper supplements the proposed model with known battery types, which improves the utilization of the available capacity of BSSs. Finally, numerical results verify the effectiveness of the proposed model. Note to Practitioners-Electric vehicles (EVs) are becoming an alternative way to reduce carbon emissions in transportation systems. Herein, the optimal battery charging problem is the core problem when it comes to dispatching a huge number of EVs. Up to now, battery-swapping is widely used for EVs due to its simple, convenient way. Furthermore, a business model for the battery swapping stations (BSSs) is brought up, where EV users send their depleted batteries to the BSS and the BSS provides the users with a fully charged replacement battery from its warehouse, which only takes a few minutes. Since the maximum charging power of the BSS is limited by the capacity of the transformer connecting the BSS to the power grid, the BSS will adopt an optimal charging schedule that maximizes the charging benefit for large quantities of depleted batteries in the warehouse. However, the challenge is that the charging schedule for depleted batteries must be made without knowledge of future battery arrivals because the EV behaviors are difficult to predict. To address this problem, this paper developed an online charging scheduling algorithm, which formulates the charging strategy as a two-dimensional rectangle packing problem. The proposed method can provide battery replacement services in real-time and solve quickly without any information about incoming depleted EV batteries. The proposed model and method have been tested on the system with different numbers of batteries to show the effectiveness. Besides, the online two-dimensional rectangle packing problem can provide an online decision for BSSs.

Keyword :

battery swapping station Electric vehicles online algorithm rectangle packing problem uninterrupted discrete-rate charging

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GB/T 7714 Bai, Jiawen , Ding, Tao , Jia, Wenhao et al. Online Rectangle Packing Algorithm for Swapped Battery Charging Dispatch Model Considering Continuous Charging Power [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2022 .
MLA Bai, Jiawen et al. "Online Rectangle Packing Algorithm for Swapped Battery Charging Dispatch Model Considering Continuous Charging Power" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2022) .
APA Bai, Jiawen , Ding, Tao , Jia, Wenhao , Zhu, Shanying , Bai, Linquan , Li, Fangxing . Online Rectangle Packing Algorithm for Swapped Battery Charging Dispatch Model Considering Continuous Charging Power . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2022 .
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Carbon Emission Prediction of Thermal Power Plants Based on Machine Learning Techniques EI Scopus
会议论文 | 2022 , 1142-1146 | 5th International Conference on Energy, Electrical and Power Engineering, CEEPE 2022
SCOPUS Cited Count: 4
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Abstract :

Since the magnificent goal of Peak Carbon Dioxide Emissions and Carbon Neutrality was put forward in 2020, carbon emission reduction has attracted unprecedented attention. The power industry must fulfill its carbon emission reduction obligations as soon as possible. Thermal power plants are the main source of carbon emissions in the power industry, so finding out the key influencing factors of thermal-power-plant carbon emission and making accurate predictions are important measures to promote the low-carbon development of the power industry. Although some precise models have been proposed, most power plants cannot obtain all the parameters required by the precise models in the actual production practice, which limits their application. Machine learning technology accepts numerical data as input and establishes the mapping relationship between variables automatically, which results in loose requirements on data. This paper summarizes several key influencing factors of carbon dioxide emissions of thermal power plants that are easy to observe and establishes a prediction model of carbon dioxide emissions of thermal power plants based on eXtreme Gradient Boosting. In addition, we compare our method with two machine learning methods proposed in previous research and obtain a satisfactory result. © 2022 IEEE.

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GB/T 7714 Zhu, Chao , Shi, Peng , Li, Zhuang et al. Carbon Emission Prediction of Thermal Power Plants Based on Machine Learning Techniques [C] . 2022 : 1142-1146 .
MLA Zhu, Chao et al. "Carbon Emission Prediction of Thermal Power Plants Based on Machine Learning Techniques" . (2022) : 1142-1146 .
APA Zhu, Chao , Shi, Peng , Li, Zhuang , Li, Mingle , Zhang, Hongji , Ding, Tao . Carbon Emission Prediction of Thermal Power Plants Based on Machine Learning Techniques . (2022) : 1142-1146 .
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