Advances in Energy and AI

In physics, energy is the quantitative property that is transferred to a body or to a physical system, recognizable in the performance of work and in the form of heat and light. Energy is a conserved quantity—the law of conservation of energy states that energy can be converted in form, but not created or destroyed. The unit of measurement for energy in the International System of Units is the joule.

"AI" redirects here. For other uses, see AI (disambiguation), Artificial intelligence (disambiguation), and Intelligent agent. Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is a field of study in computer science that develops and studies intelligent machines. Such machines may be called AIs.

In the present book, nine typical literatures about Energy and AI published on international authoritative journals were selected to introduce the worldwide newest progress, which contains reviews or original researches on VISION-iT, a vision driven sensor, Synthetic demand data generation, An explainable AI model, Data-driven comparison, ect. We hope this book can demonstrate advances in Energy and AI as well as give references to the researchers, students and other related people.

Components of the Book:
  • preface
  • Chapter 1
    VISION-iT: A Framework for Digitizing Bubbles and Droplets
  • Chapter 2
    Development and evaluation of a vision driven sensor for estimating fuel feeding rates in combustion and gasification processes
  • Chapter 3
    Synthetic demand data generation for individual electricity consumers:Inpainting
  • Chapter 4
    An explainable AI model for power plant NOx emission control
  • Chapter 5
    Development and assessment of artificial neural network models for direct normal solar irradiance forecasting using operational numerical weather prediction data
  • Chapter 6
    Data-driven comparison of federated learning and model personalization for electric load forecasting
  • Chapter 7
    Determination of broadband atmospheric turbidity from global irradiance or photovoltaic power data using deep neural nets
  • Chapter 8
    Identifying representative days of solar irradiance and wind speed in Brazil using machine learning techniques
  • Chapter 9
    A machine learning framework for remaining useful lifetime prediction of li-ion batteries using diverse neural networks
Readership: Students, academics, teachers and other people attending or interested in Energy and AI.
Youngjoon Suh
Department of Mechanical and Aerospace Engineering, University of California, Irvine, 4200 Engineering Gateway, CA 92617-2700, USA.

Henrik Wiinikka
Energy Engineering, Division of Energy Science, Lule University of Technology, SE 97187, Lule, Sweden.

Hans-Georg Stark
Technische Hochschule Aschaffenburg, Würzburger Strae 45, 63743 Aschaffenburg, Germany.

Ioanna Aslanidou
School of Innovation, Design and Engineering, Malardalens University, Universitetsplan 1, 722 20, Vasters, Sweden.

Dax K. Matthews
University of Hawaii at Manoa, Hawaii Natural Energy Institute (HNEI), 1680 East West Road, POST 109, Honolulu, 96822, HI, USA.

Rafaela Ribeiro
Industrial Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), 22451-900, Rio de Janeiro, RJ, Brazi.

and more...
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