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Paperback. Condition: New. Big data raises new opportunities for deep insights and supporting decision-making. To seize these opportunities, methods that derive useful knowledge from large amounts of data are needed. Such methods can help meet urgent challenges in many fields. An urgent challenge for energy systems is the necessary transformation towards sustainability to mitigate climate change. One crucial aspect of this challenge is a permanent optimal operation of energy systems. In principle, mathematical optimization can best determine the optimal operation of energy systems. However, manual model generation and operational optimization of energy systems are time-consuming and can thus prevent an application of mathematical optimization in practice. This thesis presents methods that use measured data to automatically generate mathematical models of energy systems to tackle the challenge of time-consuming model generations. Additionally, methods are presented that accelerate the operational optimization of energy systems. Regarding model generation, the presented data-driven methods solve the trade-off between accuracy and computational efficiency of the energy system model by weighting each component model by its role in the overall system. Thereby, the methods automatically generate energy system models that allow for accurate and computationally efficient optimization.To accelerate the operational optimization of energy systems, we present two methods that decompose the complex operational optimization problem into smaller subproblems. The methods provide high-quality solutions. The first method employs expert knowledge about the individual energy system to significantly accelerate the operational optimization while retaining an excellent solution quality.The second method applies artificial neural nets to solve the operational optimization in a reliably short time while offering a high solution quality. Overall, the methods presented in this thesis enable a broader application of mathematical optimization for energy systems.
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X, 355 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.
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Published by LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6207640683 ISBN 13: 9786207640683
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Published by China Electric Power Press, 2024
ISBN 10: 7519893138 ISBN 13: 9787519893132
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paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2024-10 Pages: 64 Publisher: China Electric Power Press With the continuous development of science and technology. the combination of advanced power generation technology and solid waste energy conversion (hereinafter referred to as solid waste energy conversion) to form multi-source solid waste coupled power generation systems will play a greater role in solving the growing urban solid waste problem. improve power generation systems. and contribute to ac.
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Condition: New. *Price HAS BEEN REDUCED by 10% until Monday, June 1 (SALE item)* 268 pp., Hardcover, new. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.
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Published by LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6207640683 ISBN 13: 9786207640683
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Taschenbuch. Condition: Neu. Multi-Source Inverter for the Control of Hybrid Energy Storage Systems | Balraj Baskaran | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786207640683 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book demonstrates how Multi-Criteria Decision Making (MCDM) and Geographic Information Systems (GIS) can revolutionize sustainable energy planning. From evaluating and prioritizing criteria for optimal site selection to harnessing the power of GIS for spatial analysis and visualization, readers of the book learn about decision-making processes that drive renewable energy projects. The book includes theory and practical application, and includes illustrated maps and visualizations generated through GIS. It offers a robust foundation for professionals, researchers, and students.
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