000			06285nam a2200925 i 4500
001			7985005
003			IEEE
005			20191218152128.0
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.
650		7	_aElectric power systems. _2fast
655		4	_aElectronic books.
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.
710	2		_aIEEE Xplore (Online Service), _edistributor.
710	2		_aWiley, _epublisher.
776	0	8	_iPrint version: _z9781118171349
830		0	_aIEEE Press series on power engineering.
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=7985005
999			_c42679 _d42679