000 06350nam a2200937 i 4500
001 7985005
003 IEEE
005 20220712205949.0
006 m o d
007 cr |n|||||||||
008 170801s2017 mau ob 001 eng d
019 _a990777980
020 _a9781119260295
_qelectronic bk. : oBook
020 _a9781119308966
020 _a1119308968
020 _z9781118171349
_qprint
020 _z1119260299
_qelectronic bk. : oBook
024 7 _a10.1002/9781119260295
_2doi
035 _a(CaBNVSL)mat07985005
035 _a(IDAMS)0b00006485e186cd
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aTK1001
082 0 4 _a621.31
_223
245 0 0 _aAdvances in electric power and energy systems :
_bload and price forecasting /
_cedited by Mohamed E. El-Hawary.
264 1 _aHoboken, New Jersey :
_bWiley,
_c2017.
264 2 _a[Piscataqay, New Jersey] :
_bIEEE Xplore,
_c[2017]
300 _a1 PDF (328 pages).
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aIEEE Press series on power engineering
500 _aIncludes index.
504 _aIncludes bibliographical references and index.
505 0 _aIntroduction / Mohamed E El-Hawary -- Univariate Methods for Short-Term Load Forecasting / James W Taylor, Patrick E McSharry -- Application of the Weighted Nearest Neighbor Method to Power System Forecasting Problems / Antonio G�aomez-Exp�aosito, Alicia Troncoso, Jes�aus M Riquelme-Santos, Catalina G�aomez-Quiles, Jos�ae L Mart�ainez-Ramos, Jos�ae C Riquelme -- Electricity Prices as a Stochastic Process / Yunhe Hou, Chen-Ching Liu, Harold Salazar -- Short-Term Forecasting of Electricity Prices Using Mixed Models / Carolina Garc�aia-Martos, Julio Rodr�aiguez, Mar�aia Jes�aus S�aanchez -- Electricity Price Forecasting Using Neural Networks and Similar Days / Paras Mandal, Anurag K Srivastava, Tomonobu Senjyu, Michael Negnevitsky -- Estimation of Post-Storm Restoration Times for Electric Power Distribution Systems / Rachel A Davidson, Haibin Liu, Tatiyana V Apanasovich -- A Nonparametric Approach for River Flow Forecasting Based on Autonomous Neural Network Models / Vitor Hugo Ferreira, Alexandre P Alves da Silva.
506 _aRestricted to subscribers or individual electronic text purchasers.
520 _aA comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world's foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial arenas. Short-run forecasting of electricity prices has become necessary for power generation unit schedule, since it is the basis of every maximization strategy. This book fills a gap in the literature on this increasingly important topic. Following an introductory chapter offering background information necessary for a full understanding of the forecasting issues covered, this book: . Introduces advanced methods of time series forecasting, as well as neural networks. Provides in-depth coverage of state-of-the-art power system load forecasting and electricity price forecasting . Addresses river flow forecasting based on autonomous neural network models. Deals with price forecasting in a competitive market. Includes estimation of post-storm restoration times for electric power distribution systems. Features contributions from world-renowned experts sharing their insights and expertise in a series of self-contained chapters Advances in Electric Power and Energy Systems is a valuable resource for practicing engineers, regulators, planners, and consultants working in or concerned with the electric power industry. It is also a must read for senior undergraduates, graduate students, and researchers involved in power system planning and operation.
530 _aAlso available in print.
538 _aMode of access: World Wide Web
588 _aOnline resource; title from PDF title page (John Wiley, viewed June 21, 2017).
650 0 _aElectric power systems.
_94058
650 7 _aElectric power systems.
_2fast
_94058
655 4 _aElectronic books.
_93294
695 _aAnalytical models
695 _aArtificial neural networks
695 _aAutoregressive processes
695 _aBiological system modeling
695 _aClustering algorithms
695 _aCompanies
695 _aComplexity theory
695 _aComputational modeling
695 _aData mining
695 _aData models
695 _aElectricity supply industry
695 _aEstimation
695 _aEurope
695 _aForecasting
695 _aHidden Markov models
695 _aHurricanes
695 _aIce
695 _aLoad forecasting
695 _aLoad modeling
695 _aMathematical model
695 _aMeteorology
695 _aNeural networks
695 _aNumerical models
695 _aPower markets
695 _aPower system faults
695 _aPower systems
695 _aPredictive models
695 _aProbability density function
695 _aStochastic processes
695 _aStorms
695 _aTime series analysis
700 1 _aEl-Hawary, M. E.,
_eeditor.
_927047
710 2 _aIEEE Xplore (Online Service),
_edistributor.
_929114
710 2 _aWiley,
_epublisher.
_929115
776 0 8 _iPrint version:
_z9781118171349
830 0 _aIEEE Press series on power engineering.
_97125
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=7985005
942 _cEBK
999 _c74511
_d74511