Machine Learning Applications for Intelligent Energy Management (eBook, PDF)

Invited Chapters from Experts on the Energy Field
Redaktion: Doukas, Haris; Sarmas, Elissaios; Marinakis, Vangelis

• Format: PDF

Produktbeschreibung

As carbon dioxide (CO2) emissions and other greenhouse gases constantly rise and constitute the main contributor to climate change, temperature rise and global warming, artificial intelligence, big data, Internet of things, and blockchain technologies are enlisted to help enforce energy transition and transform the entire energy sector.

The book at hand presents state-of-the-art developments in artificial intelligence-empowered analytics of energy data and artificial intelligence-empowered application development. Topics covered include a presentation of the various stakeholders in the energy sector and their corresponding required analytic services, such as state-of-the-art machine learning, artificial intelligence, and optimization models and algorithms tailored for a series of demanding energy problems and aiming at providing optimal solutions under specific constraints.

Professors, researchers, scientists, engineers, and students inenergy sector-related disciplines are expected to be inspired and benefit from this book, along with readers from other disciplines wishing to learn more about this exciting new field of research.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 226
• Erscheinungstermin: 27. Januar 2024
• Englisch
• ISBN-13: 9783031479090
• Artikelnr.: 69863468

Inhaltsangabe

AI-Powered Transformation and Decentralization of the Energy Ecosystem.- An Explainable AI-based Framework for Supporting Decisions in Energy Management.- The big data value chain for the provision of AI-enabled energy analytics services.- MODULAR BIG DATA APPLICATIONS FOR ENERGY SERVICES IN BUILDINGS AND DISTRICTS: DIGITAL TWINS, TECHNICAL BUILDING MANAGEMENT SYSTEMS AND ENERGY SAVINGS CALCULATIONS.- Neural network based approaches for fault diagnosis of photovoltaic systems.- Clustering of building stock.- BIG DATA SUPPORTED ANALYTICS FOR NEXT GENERATION ENERGY PERFORMANCE CERTIFICATES.- Synthetic data on buildings.