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020 ▼a 9780438343221
035 ▼a (MiAaPQ)AAI10843746
035 ▼a (MiAaPQ)cornellgrad:10967
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 621.3
1001 ▼a Baltaoglu, Mukadder Sevi. ▼0 (orcid)0000-0001-8769-8699
24510 ▼a Online Learning and Its Applications in Electricity Markets.
260 ▼a [S.l.] : ▼b Cornell University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 127 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
500 ▼a Adviser: Lang Tong.
5021 ▼a Thesis (Ph.D.)--Cornell University, 2018.
520 ▼a Online learning is the process of learning to make accurate predictions and optimize actions sequentially in each period based on the information gained through the previous decisions and observations. In many real-world problems, the underlying
520 ▼a We first study the problem of online learning and optimization of unknown Markov jump affine models which is motivated by the dynamic pricing problem of an electricity retailer. An online learning policy, referred to as Markovian simultaneous pe
520 ▼a Motivated by virtual trading in two-settlement wholesale electricity markets, the second problem we consider is the online learning problem of optimal bidding strategy in repeated multi-commodity auctions. A polynomial-time online learning algor
590 ▼a School code: 0058.
650 4 ▼a Electrical engineering.
650 4 ▼a Artificial intelligence.
690 ▼a 0544
690 ▼a 0800
71020 ▼a Cornell University. ▼b Electrical & Computer Engineering.
7730 ▼t Dissertation Abstracts International ▼g 80-01B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0058
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999943 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자