001440189 000__ 04570cam\a2200577\a\4500
001440189 001__ 1440189
001440189 003__ OCoLC
001440189 005__ 20230309004547.0
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001440189 008__ 211006s2021\\\\si\\\\\\ob\\\\000\0\eng\d
001440189 019__ $$a1273673601$$a1287768519$$a1292518917
001440189 020__ $$a9789811649752$$q(electronic bk.)
001440189 020__ $$a9811649758$$q(electronic bk.)
001440189 020__ $$z981164974X
001440189 020__ $$z9789811649745
001440189 0247_ $$a10.1007/978-981-16-4975-2$$2doi
001440189 035__ $$aSP(OCoLC)1273475861
001440189 040__ $$aYDX$$beng$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCF$$dDCT$$dOCLCO$$dDKU$$dOCLCQ$$dCOM$$dOCLCO$$dUKAHL$$dOCLCQ
001440189 049__ $$aISEA
001440189 050_4 $$aHD9685.A2
001440189 08204 $$a333.793/23$$223
001440189 1001_ $$aChen, Qixin$$c(Electrical engineer),$$eauthor.
001440189 24510 $$aData analytics in power markets /$$cQixin Chen, Hongye Guo, Kedi Zheng, Yi Wang.
001440189 260__ $$aSingapore :$$bSpringer,$$c2021.
001440189 300__ $$a1 online resource
001440189 336__ $$atext$$btxt$$2rdacontent
001440189 337__ $$acomputer$$bc$$2rdamedia
001440189 338__ $$aonline resource$$bcr$$2rdacarrier
001440189 347__ $$atext file
001440189 347__ $$bPDF
001440189 504__ $$aIncludes bibliographical references.
001440189 5050_ $$aIntroduction to power market data and their characteristics -- Modeling load forecasting uncertainty using deep learning models -- Data-driven load data cleaning and its impacts on forecasting performance -- Generalized cost-oriented load forecasting in economic dispatch -- A monthly electricity consumption forecasting method -- Data-driven pattern extraction for analyzing market bidding behaviors -- Stochastic optimal offering based on probabilistic forecast on aggregated supply curves -- Power market simulation framework based on learning from individual offering strategy -- Deep inverse reinforcement learning for reward function identification in bidding models -- The subspace characteristics and congestion identification of LMP data -- Online transmission topology identification in LMP-based markets -- Day-ahead componential electricity price forecasting -- Quantifying the impact of price forecasting error on market bidding -- Virtual bidding and FTR speculation based on probabilistic LMP forecasting -- Abnormal detection of LMP scenario and data with deep neural networks.
001440189 506__ $$aAccess limited to authorized users.
001440189 520__ $$aThis book aims to solve some key problems in the decision and optimization procedure for power market organizers and participants in data-driven approaches. It begins with an overview of the power market data and analyzes on their characteristics and importance for market clearing. Then, the first part of the book discusses the essential problem of bus load forecasting from the perspective of market organizers. The related works include load uncertainty modeling, bus load bad data correction, and monthly load forecasting. The following part of the book answers how much information can be obtained from public data in locational marginal price (LMP)-based markets. It introduces topics such as congestion identification, componential price forecasting, quantifying the impact of forecasting error, and financial transmission right investment. The final part of the book answers how to model the complex market bidding behaviors. Specific works include pattern extraction, aggregated supply curve forecasting, market simulation, and reward function identification in bidding. These methods are especially useful for market organizers to understand the bidding behaviors of market participants and make essential policies. It will benefit and inspire researchers, graduate students, and engineers in the related fields.
001440189 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 7, 2021).
001440189 650_0 $$aElectric utilities$$xStatistical methods.
001440189 650_0 $$aElectric utilities$$xData processing.
001440189 650_6 $$aServices publics d'électricité$$xMéthodes statistiques.
001440189 650_6 $$aServices publics d'électricité$$xInformatique.
001440189 655_0 $$aElectronic books.
001440189 7001_ $$aGuo, Hongye,$$eauthor.
001440189 7001_ $$aZheng, Kedi,$$eauthor.
001440189 7001_ $$aWang, Yi,$$eauthor.
001440189 77608 $$iPrint version:$$aChen, Qixin (Electrical engineer).$$tData analytics in power markets.$$dSingapore : Springer, 2021$$z981164974X$$z9789811649745$$w(OCoLC)1259048814
001440189 852__ $$bebk
001440189 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-4975-2$$zOnline Access$$91397441.1
001440189 909CO $$ooai:library.usi.edu:1440189$$pGLOBAL_SET
001440189 980__ $$aBIB
001440189 980__ $$aEBOOK
001440189 982__ $$aEbook
001440189 983__ $$aOnline
001440189 994__ $$a92$$bISE