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020 _a9781394249435
020 _a9781394249466
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020 _a1394249462
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020 _a1394249454
_qelectronic book
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020 _a1394249446
020 _a9781394249459
_q(electronic bk.)
020 _z9781394249435
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020 _z1394249438
_qhardcover
024 8 _aCIPO000218290
035 _a(OCoLC)1504818726
_z(OCoLC)1504744727
035 9 _a(OCLCCM-Owned)1504818726
040 _aYDX
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041 _aeng
049 _aMAIN
050 4 _aTJ808
_b.F67 2025
082 0 0 _223
_a621.042015196
245 0 0 _aForecasting methods for renewable power generation /
_cedited by Jai Govind Singh, Rupendra Kumar Pachauri, and Sasidharan Sreeedharan.
264 1 _aHoboken, NJ :
_bJohn Wiley & Sons, Inc. ;
_aBeverly, MA :
_bScrivener Publishing LLC,
_c2025.
264 4 _c©2025
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
520 _aForecasting Methods for Renewable Power Generation is an essential resource for both professionals and students, providing in-depth insights into vital forecasting techniques that enhance grid stability, optimize resource management, and enable effective electricity pricing strategies. It is a must-have reference for anyone involved in the clean energy sector. Forecasting techniques in renewable power generation, demand response, and electricity pricing are vital for grid stability, optimal resource allocation, efficient energy management, and cost-effective electricity supply. They enable grid operators and market participants to make informed decisions, mitigate risks, and enhance the overall reliability and sustainability of the electrical grid. Electricity prices can vary significantly based on supply and demand dynamics. By forecasting expected demand and the availability of generation resources, market operators can optimize electricity pricing strategies. This alignment of prices with anticipated supply-demand balance incentivizes the efficient use of electricity and promotes market efficiency. Accurate forecasting helps prevent price spikes, reduces market uncertainties, and supports the development of effective energy trading strategies. This book presents these topics and trends in an encyclopedic format, serving as a go-to reference for engineers, scientists, or students interested in the subject. The book is divided into three easy-to-navigate sections that thoroughly examine the AI and machine learning-based algorithms and pseudocode considered in this study. This is the most comprehensive and up-to-date encyclopedia of forecasting in renewable power generation, demand response, and electricity pricing ever written, and is a must-have for any library.
588 _aDescription based on online resource; title from digital title page (viewed on March 28, 2025).
650 0 _aRenewable energy sources
_xForecasting.
655 0 _aElectronic books.
700 1 _aSingh, Jai Govind,
_eeditor.
700 1 _aPachauri, Rupendra Kumar,
_eeditor.
_1https://id.oclc.org/worldcat/entity/E39PCjJ76cQMpDyVRfpjhT34v3
_0http://id.loc.gov/authorities/names/n2020067819
700 1 _aSreeedharan, Sasidharan,
_eeditor.
776 0 8 _iPrint version:
_z1394249438
_z9781394249435
_w(OCoLC)1493489295
776 0 8 _iPrint version:
_z9781394249435
856 4 0 _uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781394249466
_yFull text is available at Wiley Online Library. Click here to view.
942 _2ddc
_cER