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Power Plant Operation Optimisation: Unit commitment of gas turbines using Machine Learning and MILP programming
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Building, Energy and Environmental Engineering, Energy system.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
Sustainable development
The essay/thesis is partially on sustainable development according to the University's criteria
Alternative title
Optimización de la operación de centrales eléctricas : Asignación de unidades de turbinas de gas utilizando aprendizaje automático y programación MILP (Spanish)
Place, publisher, year, edition, pages
2018. , p. 133
Keywords [en]
Gas Turbine, CCGT, Economic Dispatch, Load Forecasting, MILP, Machine Learning, Boosted Regression Trees, Energy Systems Optimisation.
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:hig:diva-27660OAI: oai:DiVA.org:hig-27660DiVA, id: diva2:1239495
External cooperation
Siemens Industrial Turbomachinery (SIT)
Subject / course
Energy systems
Educational program
Energy systems – master’s programme (two years)
Presentation
2018-08-14, 11320, Kungsbäcksvägen 47, 801 76, Gävle, 09:30 (English)
Supervisors
Examiners
Available from: 2018-08-24 Created: 2018-08-16 Last updated: 2018-08-24Bibliographically approved

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