000			03698nam a22006135i 4500
001			978-3-031-45357-1
003			DE-He213
005			20251030110831.0
007			cr nn 008mamaa
008			231110s2024 sz | s |||| 0|eng d
020			_a9783031453571 _9978-3-031-45357-1
024	7		_a10.1007/978-3-031-45357-1 _2doi
050		4	_aTK1001-1841
072		7	_aTHR _2bicssc
072		7	_aTEC031020 _2bisacsh
072		7	_aTHR _2thema
100	1		_aLi, Fangxing. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _967120
245	1	0	_aDeep Learning for Power System Applications _h[electronic resource] : _bCase Studies Linking Artificial Intelligence and Power Systems / _cby Fangxing Li, Yan Du.
250			_a1st ed. 2024.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2024.
300			_aXII, 101 p. 32 illus., 30 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aPower Electronics and Power Systems, _x2196-3193
505	0		_aIntroduction-A Brief History of Deep Learning and Its Applications in Power Systems -- Deep Neural Network for Microgrid Management -- Deep Convolutional Neural Network for Power System N-1 Contingency Screening and Cascading Outage Screening -- Intelligent Multi-zone Residential HVAC Control Strategy Based on Deep Reinforcement Learning -- Summary and Future Works.
520			_aThis book provides readers with an in-depth review of deep learning-based techniques and discusses how they can benefit power system applications. Representative case studies of deep learning techniques in power systems are investigated and discussed, including convolutional neural networks (CNN) for power system security screening and cascading failure assessment, deep neural networks (DNN) for demand response management, and deep reinforcement learning (deep RL) for heating, ventilation, and air conditioning (HVAC) control. Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems is an ideal resource for professors, students, and industrial and government researchers in power systems, as well as practicing engineers and AI researchers. Provides a history of AI in power grid operation and planning; Introduces deep learning algorithms and applications in power systems; Includes several representative case studies.
650		0	_aElectric power production. _965675
650		0	_aElectric power distribution. _966068
650		0	_aEnergy policy. _961992
650		0	_aEnergy and state. _966460
650		0	_aArtificial intelligence. _958441
650		0	_aMachine learning. _955059
650	1	4	_aElectrical Power Engineering. _965867
650	2	4	_aEnergy Grids and Networks. _966069
650	2	4	_aEnergy Policy, Economics and Management. _966461
650	2	4	_aArtificial Intelligence. _958441
650	2	4	_aMachine Learning. _955059
700	1		_aDu, Yan. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _967121
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031453564
776	0	8	_iPrinted edition: _z9783031453588
776	0	8	_iPrinted edition: _z9783031453595
830		0	_aPower Electronics and Power Systems, _x2196-3193 _967122
856	4	0	_uhttps://doi.org/10.1007/978-3-031-45357-1
912			_aZDB-2-ENE
912			_aZDB-2-SXEN
999			_c56532 _d56532