Reprint

Computational Intelligence in Photovoltaic Systems

Edited by
September 2019
180 pages
  • ISBN978-3-03921-098-5 (Paperback)
  • ISBN978-3-03921-099-2 (PDF)

This is a Reprint of the Special Issue Computational Intelligence in Photovoltaic Systems that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

Photovoltaics, among the different renewable energy sources (RES), has become more popular. In recent years, however, many research topics have arisen as a result of the problems that are constantly faced in smart-grid and microgrid operations, such as forecasting of the output of power plant production, storage sizing, modeling, and control optimization of photovoltaic systems.

Computational intelligence algorithms (evolutionary optimization, neural networks, fuzzy logic, etc.) have become more and more popular as alternative approaches to conventional techniques for solving problems such as modeling, identification, optimization, availability prediction, forecasting, sizing, and control of stand-alone, grid-connected, and hybrid photovoltaic systems. This Special Issue will investigate the most recent developments and research on solar power systems.

This Special Issue “Computational Intelligence in Photovoltaic Systems” is highly recommended for readers with an interest in the various aspects of solar power systems, and includes 10 original research papers covering relevant progress in the following (non-exhaustive) fields:

  • Forecasting techniques (deterministic, stochastic, etc.);
  • DC/AC converter control and maximum power point tracking techniques;
  • Sizing and optimization of photovoltaic system components;
  • Photovoltaics modeling and parameter estimation;
  • Maintenance and reliability modeling;
  • Decision processes for grid operators.
Format
  • Paperback
License and Copyright
© 2019 by the authors; CC BY-NC-ND license
Keywords
demand response; genetic algorithm; renewable energy; unit commitment; uncertainty; artificial neural network; day-ahead forecast; ensemble methods; harmony search meta-heuristic algorithm; solar radiation; photovoltaic; tilt angle; orientation; smart photovoltaic system blind; prototype model; photovoltaic panel; tracking system; monitoring system; photovoltaic; battery; integrated storage; PV cell temperature; thermal model; thermal image; single-diode photovoltaic model; online diagnosis; genetic algorithm; embedded systems; photovoltaics; power forecasting; artificial neural networks; solar cell; metaheuristic algorithm; electrical parameters; analytical methods; firefly algorithm; statistical errors; photovoltaics; MPPT algorithm; evolutionary algorithms; particle swarm optimization; solar photovoltaic; parameter extraction; symbiotic organisms search; metaheuristic; computational intelligence; day-ahead forecast; photovoltaics