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Soft Computing in Green and Renewable Energy Systems

  • Book
  • © 2011

Overview

Editors:
  1. Kasthurirangan Gopalakrishnan

    1. Civil Engineering Associate, Ames Lab, US Department of Energy Research Affiliate, Iowa Bioeconomy Institute, Iowa State University, Ames, USA

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  2. Siddhartha Kumar Khaitan

    1. Department of Electrical and Computer Engineering, Iowa State University, Ames, USA

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  3. Soteris Kalogirou

    1. Department of Mechanical Engineering and Materials Sciences and Engineering, Cyprus University of Technology, Limassol, Cyprus

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  • State-of-the-art applications of soft computing techniques to green and renewable energy systems
  • Presents soft computing techniques and hybrid intelligent systems for designing, modeling, characterizing, optimizing, forecasting, and performance prediction of green and renewable energy systems
  • Written by leading experts in the field

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 269)

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Table of contents (11 chapters)

  1. Front Matter

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  2. Soft Computing Applications in Thermal Energy Systems

    • Arturo Pacheco-Vega
    Pages 1-35

  3. Use of Soft Computing Techniques in Renewable Energy Hydrogen Hybrid Systems

    • Gabriele Zini, Simone Pedrazzi, Paolo Tartarini
    Pages 37-64

  4. Soft Computing in Absorption Cooling Systems

    • Arzu Şencan Şahin, Soteris A. Kalogirou
    Pages 65-95

  5. A Comprehensive Overview of Short Term Wind Forecasting Models Based on Time Series Analysis

    • Athanasios Sfetsos
    Pages 97-116

  6. Load Flow with Uncertain Loading and Generation in Future Smart Grids

    • Olav Krause, Sebastian Lehnhoff
    Pages 117-156

  7. Evaluation of Green and Renewable Energy System Alternatives Using a Multiple Attribute Utility Model: The Case of Turkey

    • İhsan Kaya, Cengiz Kahraman
    Pages 157-182

  8. A Novel Fuzzy-Based Methodology for Biogas Fuelled Hybrid Energy Systems Decision Making

    • Alexandre Barin, Luciane N. Canha, Karine M. Magnago, Manuel A. Matos, Breno Wottrich
    Pages 183-198

  9. Two New Applications of Artificial Neural Networks: Estimation of Instantaneous Performance Ratio and of the Energy Produced by PV Generators

    • Florencia Almonacid, Catalina Rus, Pedro Pérez-Higueras, Leocadio Hontoria
    Pages 199-232

  10. Optimization of Fuzzy Logic Controller Design for Maximum Power Point Tracking in Photovoltaic Systems

    • Lawrence K. Letting, Josiah L. Munda, Yskandar Hamam
    Pages 233-260

  11. Application of Artificial Neural Networks for the Prediction of a 20-kWp Grid-Connected Photovoltaic Plant Power Output

    • Adel Mellit, Alessandro Massi Pavan, Soteris A. Kalogirou
    Pages 261-283

  12. Artificial Neural Networks for the Diagnosis and Prediction of Desert Dust Transport Episodes

    • Silas Michaelides, Filippos Tymvios, Dimitris Paronis, Adrianos Retalis
    Pages 285-304

  13. Back Matter

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Keywords

About this book

Soft Computing in Green and Renewable Energy Systems provides a practical introduction to the application of soft computing techniques and hybrid intelligent systems for designing, modeling, characterizing, optimizing, forecasting, and performance prediction of green and renewable energy systems. Research is proceeding at jet speed on renewable energy (energy derived from natural resources such as sunlight, wind, tides, rain, geothermal heat, biomass, hydrogen, etc.) as policy makers, researchers, economists, and world agencies have joined forces in finding alternative sustainable energy solutions to current critical environmental, economic, and social issues. The innovative models, environmentally benign processes, data analytics, etc. employed in renewable energy systems are computationally-intensive, non-linear and complex as well as involve a high degree of uncertainty. Soft computing technologies, such as fuzzy sets and systems, neural science and systems, evolutionary algorithms and genetic programming, and machine learning, are ideal in handling the noise, imprecision, and uncertainty in the data, and yet achieve robust, low-cost solutions. As a result, intelligent and soft computing paradigms are finding increasing applications in the study of renewable energy systems. Researchers, practitioners, undergraduate and graduate students engaged in the study of renewable energy systems will find this book very useful. 

Editors and Affiliations

  • Civil Engineering Associate, Ames Lab, US Department of Energy Research Affiliate, Iowa Bioeconomy Institute, Iowa State University, Ames, USA

    Kasthurirangan Gopalakrishnan

  • Department of Electrical and Computer Engineering, Iowa State University, Ames, USA

    Siddhartha Kumar Khaitan

  • Department of Mechanical Engineering and Materials Sciences and Engineering, Cyprus University of Technology, Limassol, Cyprus

    Soteris Kalogirou

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