Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/55688
Title: Forecasting of solar irradiance for solar power plants by artificial neural network
Authors: S. Watetakarn
S. Premrudeepreechacharn
Keywords: Energy
Engineering
Issue Date: 19-Jan-2016
Abstract: © 2015 IEEE. This paper presents solar irradiance forecasting in Mae Sariang, Mae Hongson Province, Thailand which has a solar power plant. This solar power plan is a photovoltaic (PV) with capacity power output at 4 MW. However, the adoption of solar irradiance as a power source on a global scale has not been uniform, due to by meteorological conditions, which cause the fluctuations and inconsistencies in PV power output. This paper has applied the Artificial Neural Network by Backpropagation algorithm to forecast solar irradiance. The model uses solar irradiance and meteorological data of previous 7-day period and relevant data for the training. The forecasting results predict solar irradiance in half hour increments in present day which were not used in the modeling. Simulation results have shown that the mean absolute percentage errors in the four example days of the forecasting are less than 6%.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84964944575&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/55688
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.


Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.