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|Title:||Artificial intelligence techniques for photovoltaic applications: A review||Authors:||Mellit, Adel
Kalogirou, Soteris A.
|Keywords:||Artificial intelligence;Neural network;Fuzzy logic;Genetic algorithm;Expert system;Hybrid system;DSP;FPGA;VHDL;Photovoltaic systems;Meteorological data;Modeling;Forecasting;Optimization||Category:||Mechanical Engineering;Materials Engineering||Field:||Engineering and Technology||Issue Date:||2008||Publisher:||Elsevier B. V.||Source:||Progress in Energy and Combustion Science, 2008, vol. 34, no. 5, pp. 574-632||Journal:||Progress in Energy and Combustion Science||Abstract:||Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more popular nowadays. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with nonlinear problems and once trained can perform prediction and generalization at high speed. AI-based systems are being developed and deployed worldwide in a wide variety of applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. AI has been used in different sectors, such as engineering, economics, medicine, military, marine, etc. They have also been applied for modeling, identification, optimization, prediction, forecasting and control of complex systems. The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in photovoltaic systems application. Problems presented include three areas: forecasting and modeling of meteorological data, sizing of photovoltaic systems and modeling, simulation and control of photovoltaic systems. Published literature presented in this paper show the potential of AI as design tool in photovoltaic systems.||URI:||http://ktisis.cut.ac.cy/handle/10488/83||ISSN:||0360-1285||DOI:||10.1016/j.pecs.2008.01.001||Rights:||© Elsevier 2008||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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