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Forecasting power output of PV grid connected system in Thailand without using solar radiation measurement
Author(s) -
Char Chupong,
Boonyang Plangklang
Publication year - 2011
Publication title -
energy procedia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.474
H-Index - 81
ISSN - 1876-6102
DOI - 10.1016/j.egypro.2011.09.024
Subject(s) - photovoltaic system , reliability (semiconductor) , electric power system , roof , reliability engineering , artificial neural network , grid , grid connected photovoltaic power system , meteorology , power (physics) , environmental science , engineering , computer science , automotive engineering , electrical engineering , maximum power point tracking , geography , civil engineering , artificial intelligence , physics , geodesy , quantum mechanics , inverter , voltage
PV systems have been increasingly installed worldwide in recent years. Because it produces clean energy, moreover the development of technology is continued therefore the reliability is increasing and the price is decreasing in opposite. To implement the PV system, however, a significant limitation of PV system is the uncertainty of power from the sun. This will affect the quality of the electrical system that connected. Therefore, this article will present the power forecasting of a PV system by calculating the solar radiation, collecting data from weather forecasting, and using Elman neural network to forecast by using data from PV system installed at roof top of Faculty Science and Technology Rajamangala University of Technology Thanyaburi.The results of study found that the tendency to apply this method any furthe

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