001454252 000__ 04054cam\a2200589\i\4500
001454252 001__ 1454252
001454252 003__ OCoLC
001454252 005__ 20230314003509.0
001454252 006__ m\\\\\o\\d\\\\\\\\
001454252 007__ cr\cn\nnnunnun
001454252 008__ 230130s2023\\\\si\a\\\\ob\\\\000\0\eng\d
001454252 019__ $$a1365363863
001454252 020__ $$a9789811964909$$qelectronic book
001454252 020__ $$a9811964904$$qelectronic book
001454252 020__ $$z9789811964893
001454252 020__ $$z9811964890
001454252 0247_ $$a10.1007/978-981-19-6490-9$$2doi
001454252 035__ $$aSP(OCoLC)1366494108
001454252 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dYDX
001454252 049__ $$aISEA
001454252 050_4 $$aTK1001$$b.P74 2023
001454252 08204 $$a621.31210285$$223/eng/20230130
001454252 24500 $$aPrediction techniques for renewable energy generation and load demand forecasting /$$cAnuradha Tomar, Prerna Gaur, Xiaolong Jin, editors.
001454252 264_1 $$aSingapore :$$bSpringer,$$c[2023]
001454252 300__ $$a1 online resource (204 pages) :$$billustrations (black and white, and color).
001454252 336__ $$atext$$btxt$$2rdacontent
001454252 337__ $$acomputer$$bc$$2rdamedia
001454252 338__ $$aonline resource$$bcr$$2rdacarrier
001454252 4901_ $$aLecture notes in electrical engineering ;$$vvolume 956
001454252 504__ $$aIncludes bibliographical references.
001454252 5050_ $$aArtificial Intelligence for renewable energy prediction -- Solar Power Forecasting in Photovoltaic Cells using Machine Learning -- Hybrid techniques for renewable energy prediction -- A Deep Learning-based Islanding Detection Approach by Considering the Load Demand of DGsunder Different Grid Conditions -- Quantitative forecasting techniques-Comparison of PV power production estimation methods under non-homogenous temperature distribution for CPVT systems -- Renewable Energy Predictions: Worldwide Research Trends and Future perspective -- Models in Load forecasting -- Machine Learning techniques for Load forecasting -- Hybrid techniques for Load forecasting-Time Load Forecasting: A smarter expertise through modern methods -- Deep Learning techniques for Load forecasting.
001454252 506__ $$aAccess limited to authorized users.
001454252 520__ $$aThis book provides an introduction to forecasting methods for renewable energy sources integrated with existing grid. It consists of two sections; the first one is on the generation side forecasting methods, while the second section deals with the different ways of load forecasting. It broadly includes artificial intelligence, machine learning, hybrid techniques and other state-of-the-art techniques for renewable energy and load predictions. The book reflects the state of the art in distributed generation system and future microgrids and covers theory, algorithms, simulations and case studies. It offers invaluable insights through this valuable resource to students and researchers working in the fields of renewable energy, integration of renewable energy with existing grid and electrical distribution network.
001454252 588__ $$aDescription based on online resource; title from digital title page (viewed on March 08, 2023).
001454252 650_0 $$aElectric power production$$xForecasting.
001454252 650_0 $$aElectric power production$$xData processing.
001454252 650_0 $$aRenewable energy sources$$xForecasting.
001454252 650_0 $$aRenewable energy sources$$xData processing.
001454252 650_0 $$aElectric power systems$$xMathematical models.
001454252 650_0 $$aElectric power systems$$xLoad dispatching.
001454252 655_0 $$aElectronic books.
001454252 7001_ $$aTomar, Anuradha,$$eeditor.$$1https://isni.org/isni/0000000493303915
001454252 7001_ $$aGaur, Prerna,$$eeditor.
001454252 7001_ $$aJin, Xiaolong$$c(Electrical engineer),$$eeditor.
001454252 77608 $$iPrint version:$$tPrediction techniques for renewable energy generation and load demand forecasting.$$dSingapore : Springer Nature Singapore, 2023$$z9789811964893$$w(OCoLC)1359608617
001454252 830_0 $$aLecture notes in electrical engineering ;$$vv. 956.
001454252 852__ $$bebk
001454252 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-6490-9$$zOnline Access$$91397441.1
001454252 909CO $$ooai:library.usi.edu:1454252$$pGLOBAL_SET
001454252 980__ $$aBIB
001454252 980__ $$aEBOOK
001454252 982__ $$aEbook
001454252 983__ $$aOnline
001454252 994__ $$a92$$bISE