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Energy storage resources management:Planning,operation,and business model

Frontiers of Engineering Management 2022年第3期9卷 373-391页

作者： Kaile ZHOU Zenghui ZHANG Lu LIU Shanlin YANG School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education Hefei University of TechnologyHefei 230009China

With the acceleration of supply-side renewable energy penetration rate and the increasingly diversified and complex demand-side loads,how to maintain the stable,reliable,and efficient operation of the power system has become a challenging issue requiring *** of the feasible solutions is deploying the energy storage system(ESS)to integrate with the energy system to stabilize ***,considering the costs and the input/output characteristics of ESS,both theinitial configuration process and the actual operation process require efficient *** study presents a comprehensive reviewof managing ESs from the perspectives of planning,operation,and business *** of all,in terms of planning and configuration,it is investigated from capacity planning,location planning,as well as capacity and location combined *** process is generally the first step in deploying ***,it explores operation management of ESS from the perspectives of state assessment and operation *** so-called state assessment refers to the assessment of three aspects:The state of charge(SOC),the state of health(SOH),and the remaining useful life(RUL).The operation optimization includes ESS operation strategy optimization and joint operation ***,it discusses the business models of *** business models involve ancillary services and load transfer,while emerging business models include electric vehicle(EV)as energy storage and shared energy storage.

关键词： energy storage system energy storage resources management planning configuration operational management business model

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Application of blockchain in the field of intelligent manufacturing:Theoretical basis,realistic plights,and development suggestions

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Frontiers of Engineering Management 2020年第4期7卷 578-591页

作者： Qiang ZHANG Baoyu LIAO Shanlin YANG School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization and Intelligent Decision-making(Ministry of Education) Hefei University of TechnologyHefei 230009China

Blockchain technology is considered one of the promising technologies of the information technology *** core features of blockchain,such as decentralization,transparency,high security,and tamper-proof nature,bring great convenience for large-scale social cooperation and data *** has a broad application prospect in the field of intelligent *** key issues of this field,such as distributed collaborative production,industrial big data sharing and security,transparent logistics,and supply chain,are naturally consistent with the core characteristics of the blockchain *** study aims to analyze the application of blockchain in the field of intelligent ***,we introduce the basic connotation and applications of ***,we propose the theoretical basis for the application of blockchain in the field of intelligent ***,we point out the realistic plights and provide some suggestions to promote the application of blockchain in the field of intelligent manufacturing.

关键词： blockchain technology intelligent manufacturing networked collaborative manufacturing full lifecycle management manufacturing model innovation

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Photovoltaic Power Forecasting Based on SVM Optimized by Improved ABC 2

Photovoltaic Power Forecasting Based on SVM Optimized by Imp...

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2nd International Conference on Environmental Prevention and Pollution Control Technologies, EPPCT 2020

作者： Liu, Lin Lu, Yanyun Li, Shuqin School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China

In order to improve the accuracy of photovoltaic power output prediction, a photovoltaic power prediction method based on similar days and improved artificial bee colony support vector machine is proposed. Firstly, through calculating the Euclidean distance of history day and measured day meteorological factors to determine similar days. Secondly, select historical data of photovoltaic power output, temperature, humidity and daily radiation on the slope of similar days and temperature, humidity and daily radiation on the slope of test date as input variables of support vector machine. And we adopt the improved artificial bees colony to optimize kernel function parameters and the penalty factor of support vector machine. Finally get the output in each period of photovoltaic power prediction. The experimental results showed that the proposed method can effectively improve the prediction accuracy of photovoltaic power. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

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Industrial False Signal Recognition Based on Deep Learning with Focal Loss

Industrial False Signal Recognition Based on Deep Learning w...

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作者： Xu, Qifa Cheng, Qiliang Jiang, Cuixia Wang, Xiangxiang School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei230009 China

False signals often occur in actual industrial environments. They are however treated as abnormal ones, which will lead to unexpected equipment downtime and production loses. Consider the memory limitations of edge computing, how to recognize them accurately with lightweight models is an important but difficult work. To this end, we develop a novel scheme based on deep learning with focal loss. This scheme first converts original one-dimensional waveform signals into time-frequency graphs through continuous wavelet transform. These graphs are then fed into a convolutional neural network (CNN) for binary classification. Finally, focal loss function is introduced into the CNN model to solve the problem of different types of misclassification costs in recognizing false signals. We verify the effectiveness of this scheme on a real-world application. The results show that our scheme is a high-quality classifier with an accuracy of 98.42%, a Precision of 98.68%, a Recall of 98.18%, and a specificity of 98.68%, which is efficient in recognizing false signals and performs better than several competing models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Short-term power load forecasting based on self-adapting PSO-BP neural network model

Short-term power load forecasting based on self-adapting PSO...

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4th International Conference on Computational and Information Sciences, ICCIS 2012

作者： He, Yaoyao Xu, Qifa School of Management Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei 230009 China

ISBN: (纸本)9780769547893

To resolve the problem of short-term power load forecasting, we propose a self-adapting particle swarm optimization (PSO) algorithm to optimize the error back propagation (BP) neural network model. The proposed model is called PSO-BP model which employs PSO to adjust control parameters of BP neural network. In order to verify the performance of PSO-BP, the practical datum of a city in China are selected in the experiments. Simulation results show that our approach outperforms simple BP neural network. © 2012 IEEE.

关键词： Neural network models

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Mining organizational structure from workflow logs

Mining organizational structure from workflow logs

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2011 International Conference on e-Education, Entertainment and e-Management, ICEEE 2011

作者： Ni, Zhiwei Wang, Sha Li, Huaiying School of Management Hefei University of Technology Hefei 230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei 230009 China

ISBN: (纸本)9781457713811

Workflow mining is a concept in early era, it analyzes the workflow log in the control flow dimensions, to mining the actual process model, but ignores relationships among executors in workflow, hence it fails to effectively excavate the workflow log to find the implicit structure. In this paper, the organizational dimensions of workflow log will be the main focuses to be researched. The paper introduces executor similarity metrics and grid clustering to demonstrate the methods of the organizational structure mining based on executor similarity and organization mining based on grid clustering. Both of them are effective to extract the actual organizational structure from the workflow logs. © 2011 IEEE.

关键词： grid clustering organizational structure workflow mining

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Can constraint energy-saving policy facilitate industrial green production performance in China? 3

Can constraint energy-saving policy facilitate industrial gr...

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3rd International Conference on Advances in Energy and Environment Research, ICAEER 2018

作者： Yao, Yue Jiao, Jianling Han, Xiaofei School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei Anhui230009 China

The role of constraint energy-saving policy (ESP) playing in the coordinated development of energy conservation, emission reduction, and economic growth is of great significance to the country's sustainable progress. Applying a non-radial and non-oriented DEA model incorporating innovation output and multiple undesirable outputs, this paper measures the green production performance (GPP) of China's industrial sectors from 2002 to 2015. And then the effect of implementing energy-saving policy on GPP is investigated through Quasi-difference-in-differences (Quasi-DID) method. The results show that China's industrial GPP rises during 2002-2004 and then declines with fluctuations. Technology change (TC) is the dominant driver. Energy-saving policy positively affects industrial GPP in general yet further dynamic analysis reveals that such a positive effect remains unstable and finally manifests a reverse impact at the end of every five-year-plan period. Therefore, China should properly introduce market mechanisms, formulate comprehensive policy mix strategies to balance sustainable improvement of industrial economy and ecological environment. © The Authors, published by EDP Sciences, 2018.

关键词： Energy conservation

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Research on the operational efficiency of subscription accounts

Research on the operational efficiency of subscription accou...

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2020 International Conference on E-Commerce and Internet Technology, ECIT 2020

作者： He, Jianmin Wu, Hao Liu, Tingting Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei University of Technology Hefei China School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781728159027

The operation and profit of the subscription account is an important guarantee for maintaining its own high-quality development. It constructs an operation efficiency evaluation index system, with customer acquisition input and profit acquisition input as input indicators, and customer acquisition output and profit acquisition output as output indicators. On this basis, the DEA-BCC model was used to evaluate the operational efficiency of 28 subscription accounts. Based on the analysis results, this paper puts forward the improvement measures for non-DEA effective subscription accounts. Finally, this article uses Tobit regression to explore the influencing factors of operational efficiency. The results show that only 14 subscription accounts are DEA efficiency. The quality of average non-original articles has a negative impact on operational efficiency, and the quality of average original articles and the number of daily non-original articles have a positive impact on operational efficiency. Therefore, subscription accounts should pay attention to the quality of original articles and balance the quantity and quality of articles in operation management. © 2020 IEEE.

关键词： Profitability

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Measurement method of answerer service influence for Q & a community

Measurement method of answerer service influence for Q & a c...

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2020 International Conference on E-Commerce and Internet Technology, ECIT 2020

作者： He, Jianmin Liu, Tingting Jiang, Yuanchun Li, Ying Wu, Hao Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei University of Technology Hefei China School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781728159027

In this paper, we explore the method of measuring answerer service influence on Q & A community in order to provide decision support for communities to obtain paid consultation questioners and high service influence answerers. From the social capital measuring angle, this paper selects three-dimensional indicators which contain unpaid services, paid services and services quality to measure answerer service influence on Q & A community. Accordingly, we construct a social capital measurement model method for answerer service influence and demonstrate its practical effectiveness. This method can help communities discover high service influence answerers, and formulate service strategies that motivate answerers and questioners to improve the overall service efficiency and effectiveness of the community. The numerical study on the dataset from *** indicates that the proposed method can obtain reasonable measurement results. © 2020 IEEE.

关键词： Numerical methods

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Learning Adaptive Mapless Navigation Skills Through Evidential Deep Learning

Learning Adaptive Mapless Navigation Skills Through Evidenti...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Zhao, Cancan Yan, Yufeng Liu, Xuhao Zhang, Shuai Ouyang, Bo Hefei University of Technology School of Management Hefei China Hefei University of Technology The Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei China

ISBN: (纸本)9798350327182

Traditional navigation methods tend to design and train navigation skills based on the consideration of multi-scenario applicability and perform well in general scenarios. However, this results in sub-optimal navigation performance in some context-specific scenarios, such as navigating through open or crowded scenarios. While some existing navigation skills can achieve satisfactory performance in context-specific scenarios, e.g., using PID in open scenarios and reinforcement learning-based training methods in crowded scenarios. Most methods currently do without consideration of the integration of navigation skills and contextual understanding. To address this gap, we propose an adaptive mapless navigation method to utilize the optimal capabilities of each skill corresponding to the context-specific scenario. Specifically, we package several existing navigation skills and train a scenario classifier to enable the agent to automatically select the most appropriate skill based on the current scenario. Furthermore, we incorporate evidential deep learning (EDL) to assess classification uncertainty and ensure the safe switching of navigation skills. Our method was tested in both simulated and real scenarios, and the results of the experiments confirmed the effectiveness and generalization of our method. Moreover, the proposed method exhibits notably enhanced, context-sensitive efficiency within a hybrid multi-scenario setting, in comparison to a single navigation skill. © 2023 IEEE.

关键词： Navigation

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