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International Journal of Chemical Engineering
Volume 2016, Article ID 1031943, 8 pages
http://dx.doi.org/10.1155/2016/1031943
Research Article

Power Prediction and Technoeconomic Analysis of a Solar PV Power Plant by MLP-ABC and COMFAR III, considering Cloudy Weather Conditions

1Department of Applied Mathematics, Islamic Azad University, South Tehran Branch, No. 209, North Iranshahr Street, Tehran 11365-4435, Iran
2Department of Energy Systems Engineering, Islamic Azad University, South Tehran Branch, No. 209, North Iranshahr Street, Tehran 11365-4435, Iran
3Islamic Azad University, South Tehran Branch, No. 209, North Iranshahr Street, Tehran 11365-4435, Iran

Received 4 December 2015; Revised 24 January 2016; Accepted 31 January 2016

Academic Editor: Pouria Ahmadi

Copyright © 2016 M. Khademi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The prediction of power generated by photovoltaic (PV) panels in different climates is of great importance. The aim of this paper is to predict the output power of a 3.2 kW PV power plant using the MLP-ABC (multilayer perceptron-artificial bee colony) algorithm. Experimental data (ambient temperature, solar radiation, and relative humidity) was gathered at five-minute intervals from Tehran University’s PV Power Plant from September 22nd, 2012, to January 14th, 2013. Following data validation, 10665 data sets, equivalent to 35 days, were used in the analysis. The output power was predicted using the MLP-ABC algorithm with the mean absolute percentage error (MAPE), the mean bias error (MBE), and correlation coefficient (), of 3.7, 3.1, and 94.7%, respectively. The optimized configuration of the network consisted of two hidden layers. The first layer had four neurons and the second had two neurons. A detailed economic analysis is also presented for sunny and cloudy weather conditions using COMFAR III software. A detailed cost analysis indicated that the total investment’s payback period would be 3.83 years in sunny periods and 4.08 years in cloudy periods. The results showed that the solar PV power plant is feasible from an economic point of view in both cloudy and sunny weather conditions.