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Research Papers

A New Approach for Meteorological Variables Prediction at Kuala Lumpur, Malaysia, Using Artificial Neural Networks: Application for Sizing and Maintaining Photovoltaic Systems

[+] Author and Article Information
Tamer Khatib, Azah Mohamed

Department of Electrical, Electronic and System Engineering,Faculty of Engineering and Built Environment,  University Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysiatamer_khat@hotmail.com

M. Mahmoud

Department of Electrical Engineering,Engineering Faculty,  An-Najah National University, Nablus 91541, Palestinemarwanma@najah.edu

K. Sopian

 Solar Energy Research Institute,  University Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysiaksopian@eng.ukm.my

J. Sol. Energy Eng 134(2), 021005 (Feb 27, 2012) (10 pages) doi:10.1115/1.4005754 History: Received February 08, 2011; Revised November 05, 2011; Published February 27, 2012; Online February 27, 2012

This research presents a new meteorological variables prediction approach for Malaysia using artificial neural networks. The developed model predicts four meteorological variables using sun shine ratio, day number, and location coordinates. These meteorological variables are solar energy, ambient temperature, wind speed, and relative humidity. However, three statistical values are used to evaluate the proposed model. These statistical values are mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). Based on results, the developed model predicts accurately the four meteorological variables. The MAPE, RMSE, and MBE in predicting solar radiation are 1.3%, 5.8 (1.8%), and 0.9 (0.3%), respectively, while the MAPE, RMSE, and MBE values for ambient temperature prediction are 1.3%, 0.4 (1.7%), and 0.1 (0.4%), respectively. In addition, the MAPE, RMSE, and MBE values in relative humidity prediction are 3.2%, 3.2, and 0.2. As for wind speed prediction, it is the worst in accuracy among the predicted variables because the MAPE, RMSE, and MBE values are 28.9%, 0.5 (31.3%), and 0.02 (1.25%). Such a developed model helps in sizing photovoltaic (PV) systems using solar energy and ambient temperature records. Moreover, wind speed and relative humidity records could be used in estimating dust concentration group which leads to dust deposition on a PV system.

Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Impact of solar energy on PV module output current and power

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Figure 2

Impact of solar radiation on PV modules efficiency

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Figure 3

IV characteristics of PV module at different temperatures

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Figure 4

PV system output power at different ambient temperatures

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Figure 5

Solar intensity reduction in response to dust deposition [40-41]

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Figure 6

Effect of dust accumulation on IV and PV characteristic curve of a PV module [45]

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Figure 7

Dust concentration groups versus wind speed/normal wind direction [47]

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Figure 8

Dust concentration groups versus wind speed/parallel wind direction [47]

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Figure 9

Proposed ANN based weather station model

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Figure 10

A comparison between the used solar radiation data and the data obtained by NASA

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Figure 11

A comparison between the used ambient temperature data and the data obtained by NASA

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Figure 12

Prediction results for Jan. 2000

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Figure 13

Prediction results for Mar. 2001

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Figure 14

Prediction results for May 2002

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Figure 15

Prediction results for Jul. 2003

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Figure 16

Prediction results for Sep. 2004

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Figure 17

Prediction results for Nov. 2005

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