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A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings

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Building Simulation 2021年第1期14卷 131-147页

作者： Marco Savino Piscitelli Silvio Brandi Alfonso Capozzoli Fu Xiao Department of Energy“Galileo Ferraris” TEBE research groupPolitecnico di TorinoTurinItaly Department of Building Services Engineering The Hong Kong Polytechnic UniversityHong KongChina

In this paper,a tool for the detection and diagnosis of anomalous electrical daily energy patterns relative to a transformer substation of a university campus was developed and *** an innovative pattern recognition analysis consisting in a multi-step clustering process,six clusters of anomalous daily load profiles were identified and isolated in two-year historical data of total electrical energy *** infrequent electrical load profiles were found to be strongly affected,in terms of both shape and magnitude,by the energy consumption behaviour related to the heating/cooling mechanical ***,a fault-free predictive model,which uses artificial neural network(ANN)in combination with a Regression Tree,was developed to detect anomalous trends of the electrical energy *** model was able to detect the 93.7%of the anomalous profiles and only the 5%of fault-free days were wrongly predicted as ***,a diagnosis phase was conceived and validated with a testing data set.A number of daily abnormal load profiles were detected and compared with the centroids of the anomalous clusters identified in the pattern-recognition *** work led to the development of a flexible intelligent tool useful for operating a continuous commissioning of the campus facilities.

关键词： anomaly detection data analytics energy management pattern recognition prediction models

Learning-based data analytics:Moving Towards Transparent Power Grids

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CSEE Journal of Power and Energy Systems 2018年第1期4卷 67-82页

作者： Kunjin Chen Ziyu He Shan X.Wang Jun Hu Licheng Li Jinliang He State Key Lab of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China the Department of Industrial and Systems Engineering University of Southern CaliforniaLos AngelesCA 90007USA the Center for Magnetic Nanotechnology Stanford UniversityStanfordCA 94305 USAand also with the State Key Laboratory of Power SystemsDepartment of Electrical EngineeringTsinghua UniversityBeijing 100084China e Graduate School at Shenzhen Tsinghua UniversityShenzhen 518055Chinaand China Southern Power GridGuangzhou 510623China

this paper,we present the learning-based data analytics moving towards transparent power grids and provide some possible extensions including machine learning,big data analytics,and knowledge *** closed loops of data and knowledge are illustrated and the challenges for establishing the closed loops are *** ideas and recent developments in supervised learning,unsupervised learning,and reinforcement learning are presented together with extensions for power system ***,much emphasis is placed on privacypreserving data analysis,transfer of knowledge,machine learning for causal inference,scalability and flexibility of data analytics,and efficiency and reliability of *** integrated solutions in the industry featuring the Industrial Internet and the digital grid are also introduced.

关键词： data analytics machine learning smart grid transparent power grid

Should competing sellers adopt the data analytics service to enable product improvement in online marketplace

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Journal of Management Science and Engineering 2024年第3期9卷 390-418页

作者： Danyu Liang Minqiang Li Haiyang Feng Nan Feng College of Management and Economics Tianjin UniversityTianjin300072China Laboratory of CACMS Tianjin UniversityTianjin300072China

More and more marketplace platforms choose to use the data gathered from consumers(e.g.,customer search terms,demographics)to provide a data analytics service for third-party sellers,both encouraging innovations and improving the operations of the *** adopting the data analytics service,a seller can enhance its competitiveness by improving the quality of its products when there is more than one seller offering *** study develops a game-theoretic model to characterize a scenario with a marketplace platform and two competing sellers that sell substitutable products and decide whether to adopt the data analytics service provided by the ***,we find that a seller's decision of whether or not to purchase the service depends on the competitor's decision and the two sellers'absorptive capacities of ***,a seller can benefit from adopting the data analytics service only if the seller has a high absorptive capacity,which can help increase product *** only one seller adopts,the platform and competing sellers can benefit from the adoption if this seller's absorptive capacity is high and the other's is *** sellers'interests and social welfare can be aligned unless both sellers'absorptive capacities are low or high.

关键词： Marketplace platform data analytics Seller's adoption strategy Absorptive capacity Duopoly market

data analytics and optimization for smart industry

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Frontiers of Engineering Management 2021年第2期8卷 157-171页

作者： Lixin TANG Ying MENG Key Laboratory of data analytics and Optimization for Smart Industry Ministry of EducationNortheastern UniversityShenyang 110819China

Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional *** by the major development strategies and needs of industrial intellectualization in China,this study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization,as well as their application to smart industrial ***,this study describes a general methodology for the fusion of data analytics and ***,it identifies some data analytics and system optimization technologies to handle key issues in smart ***,it provides a four-level framework for smart industry based on the theoretical and technological research on the fusion of data analytics and *** framework uses data analytics to perceive and analyze industrial production and logistics *** also demonstrates the intelligent capability of planning,scheduling,operation optimization,and optimal *** analytics and system optimization technologies are employed in the four-level framework to overcome some critical issues commonly faced by manufacturing,resources and materials,energy,and logistics systems,such as high energy consumption,high costs,low energy efficiency,low resource utilization,and serious environmental *** fusion of data analytics and optimization allows enterprises to enhance the prediction and control of unknown areas and discover hidden knowledge to improve decision-making efficiency。Therefore,industrial intelligence has great importance in China’s industrial upgrading and transformation into a true industrial power.

关键词： data analytics system optimization smart industry

Big data, data analytics and External Auditing

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Journal of Modern Accounting and Auditing 2020年第5期16卷 211-219页

作者： Dimitris Balios Panagiotis Kotsilaras Nikolaos Eriotis Dimitrios Vasiliou National and Kapodistrian University of Athens AthensGreece

Big data and data analytics affect almost all aspects of modern organisations’decision-making and business *** data and data analytics create opportunities,challenges,and implications for the external auditing *** purpose of this article is to reveal essential aspects of the impact of Big data and data analytics on external *** seems that Big data analytics is a critical tool for organisations,as well as auditors,that contributes to the enhancement of the auditing ***,legislative implications must be taken under consideration,since existing standards may need to ***,auditors need to develop new skills and competence,and educational organisations need to change their educational programs in order to be able to correspond to new market needs.

关键词： Big data data analytics auditing accounting

Case Study on data analytics and Machine Learning Accuracy

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Journal of data Analysis and Information Processing 2021年第4期9卷 249-270页

作者： Abdullah Z. Alruhaymi Charles J. Kim Department of Electrical Engineering and Computer Science Howard University Washington D.C USA

The information gained after the data analysis is vital to implement its outcomes to optimize processes and systems for more straightforward problem-solving. Therefore, the first step of data analytics deals with identifying data requirements, mainly how the data should be grouped or labeled. For example, for data about Cybersecurity in organizations, grouping can be done into categories such as DOS denial of services, unauthorized access from local or remote, and surveillance and another probing. Next, after identifying the groups, a researcher or whoever carrying out the data analytics goes out into the field and primarily collects the data. The data collected is then organized in an orderly fashion to enable easy analysis;we aim to study different articles and compare performances for each algorithm to choose the best suitable classifies.

关键词： data analytics Machine Learning Accuracy Cybersecurity Performance

Intelligent data analytics is here to change engineering management

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Frontiers of Engineering Management 2017年第1期4卷 41-48页

作者： Jonathan Jingsheng SHI Saixing ZENG Xiaohua MENG College of Engineering Louisiana State University Antai College of Economics & Management Shanghai Jiao Tong University Department of Management Science and Engineering Soochow University

A great deal of scientific research in the world aims at discovering the facts about the world so that we understand it better and find solutions to problems. data enabling technology plays an important role in modern scientific discovery and technologic advancement. The importance of good information was long recognized by prominent leaders such as Sun Tzu and Napoleon. Factual data enables managers to measure, to understand their businesses, and to directly translate that knowledge into improved decision making and performance. This position paper argues that data analytics is ready to change engineering management in the following areas: 1) by making relevant historical data available to the manager at the time when it's needed; 2) by filtering out actionable intelligence from the ocean of data; and 3) by integrating useful data from multiple sources to support quantitative decision-making. Considering the unique need for engineering management, the paper proposes researchable topics in the two broad areas of data acquisition and data analytics. The purpose of the paper is to provoke discussion from peers and to encourage research activity.

关键词： engineering management project management big data data analytics planning execution

data-driven Distributed analytics and Control Platform for Smart Grid Situational Awareness

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CSEE Journal of Power and Energy Systems 2016年第3期2卷 51-58页

作者： Christopher S.Saunders Guangyi Liu Yang Yu Wendong Zhu Global Energy Interconnection Research Institute North AmericaSanta ClaraCA95054USA Stanford University 450 Serra MallStanfordCA 94305USA

A conceptual,data-driven framework for organizing the data analytics and control functions in an electrical distribution network is proposed in this *** framework is built such that it tightly corresponds to the naturally existing physical hierarchy of typical radial distribution networks,allowing for an organized and highly-localized set of data storage and analytics processes,which in turn correspond well to likely control *** utilizing this structure,the computational entities in the framework are endowed with persistent local situational ***,the framework also permits,through a series of tiered communications,the operation of a centralized authority for overall system observability and controllability.

关键词： data analytics data warehousing distributed control distributed systems machine learning situational awareness

Business challenges and research directions of management analytics in the big data era

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Journal of Management analytics 2014年第3期1卷 169-174页

作者： J.Leon Zhao Shaokun Fan Daning Hu City University of Hong Kong China West Texas A&M University CanyonUSA University of Zurich ZurichSwitzerland

Big data analytics have been embraced as a disruptive technology that will reshape business intelligence,particularly marketing intelligence,which has have traditionally relied on market surveys to understand consumer behavior and product *** this paper,we investigate how big data analytics will affect the landscape of business intelligence,leading to big data *** in the recent literature,we delineate business opportunities and managerial challenges brought forward by the emergence of big data analytics and outline a number of research directions in big data intelligence for business.

关键词： big data business intelligence data analytics data science marketing intelligence