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Computational Intelligence Magazine, IEEE

Issue 3 • Date Aug. 2011

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Displaying Results 1 - 19 of 19
  • [Front cover]

    Publication Year: 2011 , Page(s): C1
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  • 2012 IEEE World Congress on Computational Intelligence

    Publication Year: 2011 , Page(s): C2
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  • [Table of Contents]

    Publication Year: 2011 , Page(s): 1
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  • Nothing's Too Small to Have an Impact [Editor's Remarks]

    Publication Year: 2011 , Page(s): 2
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  • Global Outreach [President's Message]

    Publication Year: 2011 , Page(s): 3 - 11
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  • IEEE CIS Webinars Activities and Future Plan [Society Briefs]

    Publication Year: 2011 , Page(s): 4 - 5
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  • What it Means to Be a Young CI Researcher in the 21st Century [Society Briefs]

    Publication Year: 2011 , Page(s): 6 - 7
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  • IEEE Computational Intelligence Society Winter School 2011 [Society Briefs]

    Publication Year: 2011 , Page(s): 8
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  • In Memoriam: Dr. Herbert E. Rauch October 6, 1935–March 29, 2011 [Obituary]

    Publication Year: 2011 , Page(s): 9 - 59
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  • CIS Publication Spotlight [Publication Spotlight]

    Publication Year: 2011 , Page(s): 10 - 11
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  • Special Issue on Computational Intelligence in Smart Grid [Guest Editorial]

    Publication Year: 2011 , Page(s): 12 - 64
    Cited by:  Papers (18)
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  • IEEE Transactions on Computational Intelligence and Al in Games

    Publication Year: 2011 , Page(s): 13
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  • Computational Intelligence for the Smart Grid-History, Challenges, and Opportunities

    Publication Year: 2011 , Page(s): 14 - 21
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1637 KB) |  | HTML iconHTML  

    This paper reviews the evolution of four generations of concepts of the “smart grid,” the role of computational intelligence in meeting their needs, and key examples of relevant research and tools. The first generation focused on traditional concepts like building more wires, automated meters, workforce development, and reducing blackouts, but it already had many uses for computational intelligence. The second generation, promulgated by Massoud Amin at EPRI, entailed greater use of global control systems and stability concepts, and coincided with new issues of market design and time of day pricing. New third generation and fourth generation concepts aim for a truly intelligent power grid, addressing new requirements for a sustainable global energy system, making full use of new methods for optimization across time, pluggable electric vehicles, renewable energy, storage, distributed intelligence and new neural networks for handling complexity and stochastic challenges. Important opportunities for society and new fundamental research challenges exist throughout. View full abstract»

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  • Dynamic, Stochastic, Computational, and Scalable Technologies for Smart Grids

    Publication Year: 2011 , Page(s): 22 - 35
    Cited by:  Papers (26)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2791 KB) |  | HTML iconHTML  

    The smart electric power grid will evolve into a very complex adaptive system under semi-autonomous distributed control. Its spatial and temporal complexity, non-convexity, non-linearity, non-stationarity, variability and uncertainties exceed the characteristics found in today's traditional power system. The distributed integration of intermittent sources of energy and plug-in electric vehicles to a smart grid further adds complexity and challenges to its modeling, control and optimization. Innovative technologies are needed to handle the growing complexity of the smart grid and stochastic bidirectional optimal power flows, to maximize the penetration of renewable energy, and to provide maximum utilization of available energy storage, especially plug-in electric vehicles. View full abstract»

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  • A Belief Propagation Based Power Distribution System State Estimator

    Publication Year: 2011 , Page(s): 36 - 46
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2123 KB) |  | HTML iconHTML  

    The most popular method used in traditional power system state estimation is the Maximum Likelihood Estimation (MLE). It assumes the state of the system is a set of deterministic variables and determines the most likely state via error included interval measurements. In the distribution system, the measurements are often too sparse to fulfill the system observability. Instead of introducing pseudo-measurements, we propose a Belief Propagation (BP) based distribution system state estimator. This new approach assumes that the system state is a set of stochastic variables. With a set of prior distributions, it calculates the posterior distributions of the state variables via real-time sparse measurements from both traditional measurements and the high resolution smart metering data. In this paper we discuss the step-by-step method of applying the BP algorithm on the distribution system state estimation problem. Our approach provides a seamless connection from the monitoring of transmission system to the feeder circuit, thus filling in the gap between the traditional energy management system (EMS) and the micro-grid customer level optimization. Furthermore, the proposed state estimator can not only be applied to the multi-level electrical coupled grid, but also accommodate the spatial-temporal model for the correlated distributed renewable energy resources. It provides a way of integrating the distributed renew able energy management system into the Smart-Grid Distribution Management System (DMS) and automated substations. View full abstract»

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  • Short-Term Load Forecasting with Neural Network Ensembles: A Comparative Study [Application Notes]

    Publication Year: 2011 , Page(s): 47 - 56
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (853 KB) |  | HTML iconHTML  

    Load Forecasting plays a critical role in the management, scheduling and dispatching operations in power systems, and it concerns the prediction of energy demand in different time spans. In future electric grids, to achieve a greater control and flexibility than in actual electric grids, a reliable forecasting of load demand could help to avoid dispatch problems given by unexpected loads, and give vital information to make decisions on energy generation and purchase, especially market-based dynamic pricing strategies. Furthermore, accurate prediction would have a significant impact on operation management, e.g. preventing overloading and allowing an efficient energy storage. View full abstract»

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  • Experience of Teaching Computational Intelligence in an Undergraduate Level Course [Educational Forum]

    Publication Year: 2011 , Page(s): 57 - 59
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  • Information Theoretic Learning: Reny's Entropy and Kernel Perspectives (Principe, J.; 2010) [Book Review]

    Publication Year: 2011 , Page(s): 60 - 62
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  • [Conference Calendar]

    Publication Year: 2011 , Page(s): 63 - 64
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Aims & Scope

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). 

 

Full Aims & Scope