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Output power forecasting for 2kW monocrystalline PV system using response surface methodology

Photovoltaic (PV) system is a renewable energy source that not only able to reduce the effect of greenhouse gas towards the environment, but also a highly profitable industry nowadays. To determine the Return of Investment (ROI) of a newly installed system, forecasting is crucial. Thu...

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Main Authors: Upkli, Wenny Rumy, Wan Abdul Razak, Intan Azmira, Azmi, Aimie Nazmin, Ab Rahman, Azhan, Bohari, Zul Hasrizal
Format: Article
Language:English
Published: Penerbit Universiti Teknikal Malaysia Melaka 2019
Online Access:http://eprints.utem.edu.my/id/eprint/25301/2/5076-15356-1-PB.PDF
http://eprints.utem.edu.my/id/eprint/25301/
https://ijeeas.utem.edu.my/ijeeas/article/view/5076/pdf_35
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spelling my.utem.eprints.253012023-07-21T15:34:43Z http://eprints.utem.edu.my/id/eprint/25301/ Output power forecasting for 2kW monocrystalline PV system using response surface methodology Upkli, Wenny Rumy Wan Abdul Razak, Intan Azmira Azmi, Aimie Nazmin Ab Rahman, Azhan Bohari, Zul Hasrizal Photovoltaic (PV) system is a renewable energy source that not only able to reduce the effect of greenhouse gas towards the environment, but also a highly profitable industry nowadays. To determine the Return of Investment (ROI) of a newly installed system, forecasting is crucial. Thus, the purpose of this study is to produce a prediction model for the yearly output power of the PV system using three environmental elements; irradiance, back module temperature and ambient temperature by Response Surface Methodology (RSM). To do so, MATLAB RStool which is consisting of four models; multiple linear regression (MLR), interaction, pure quadratic, and full quadratic were used. The 5 minute sampling size of year 2014 weather station data of the three environmental elements and output power of a 2kW Monocrystalline real PV system were used for training. Whereas, year 2015 data of the aforementioned elements were used for validation. The coefficient of determination (R2) method and root mean square error (RMSE) approach were used to determine the most accurate prediction model. Results shown that, full quadratic is the most accurate prediction model with R2 value of 0.9995 and RMSE of 8%. It is hoped that the prediction model introduced can be a viable method to be used by the PV system installer. Penerbit Universiti Teknikal Malaysia Melaka 2019-10 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/25301/2/5076-15356-1-PB.PDF Upkli, Wenny Rumy and Wan Abdul Razak, Intan Azmira and Azmi, Aimie Nazmin and Ab Rahman, Azhan and Bohari, Zul Hasrizal (2019) Output power forecasting for 2kW monocrystalline PV system using response surface methodology. International Journal of Electrical Engineering and Applied Sciences (IJEEAS), 2 (2). pp. 23-32. ISSN 2600-7495 https://ijeeas.utem.edu.my/ijeeas/article/view/5076/pdf_35
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Photovoltaic (PV) system is a renewable energy source that not only able to reduce the effect of greenhouse gas towards the environment, but also a highly profitable industry nowadays. To determine the Return of Investment (ROI) of a newly installed system, forecasting is crucial. Thus, the purpose of this study is to produce a prediction model for the yearly output power of the PV system using three environmental elements; irradiance, back module temperature and ambient temperature by Response Surface Methodology (RSM). To do so, MATLAB RStool which is consisting of four models; multiple linear regression (MLR), interaction, pure quadratic, and full quadratic were used. The 5 minute sampling size of year 2014 weather station data of the three environmental elements and output power of a 2kW Monocrystalline real PV system were used for training. Whereas, year 2015 data of the aforementioned elements were used for validation. The coefficient of determination (R2) method and root mean square error (RMSE) approach were used to determine the most accurate prediction model. Results shown that, full quadratic is the most accurate prediction model with R2 value of 0.9995 and RMSE of 8%. It is hoped that the prediction model introduced can be a viable method to be used by the PV system installer.
format Article
author Upkli, Wenny Rumy
Wan Abdul Razak, Intan Azmira
Azmi, Aimie Nazmin
Ab Rahman, Azhan
Bohari, Zul Hasrizal
spellingShingle Upkli, Wenny Rumy
Wan Abdul Razak, Intan Azmira
Azmi, Aimie Nazmin
Ab Rahman, Azhan
Bohari, Zul Hasrizal
Output power forecasting for 2kW monocrystalline PV system using response surface methodology
author_facet Upkli, Wenny Rumy
Wan Abdul Razak, Intan Azmira
Azmi, Aimie Nazmin
Ab Rahman, Azhan
Bohari, Zul Hasrizal
author_sort Upkli, Wenny Rumy
title Output power forecasting for 2kW monocrystalline PV system using response surface methodology
title_short Output power forecasting for 2kW monocrystalline PV system using response surface methodology
title_full Output power forecasting for 2kW monocrystalline PV system using response surface methodology
title_fullStr Output power forecasting for 2kW monocrystalline PV system using response surface methodology
title_full_unstemmed Output power forecasting for 2kW monocrystalline PV system using response surface methodology
title_sort output power forecasting for 2kw monocrystalline pv system using response surface methodology
publisher Penerbit Universiti Teknikal Malaysia Melaka
publishDate 2019
url http://eprints.utem.edu.my/id/eprint/25301/2/5076-15356-1-PB.PDF
http://eprints.utem.edu.my/id/eprint/25301/
https://ijeeas.utem.edu.my/ijeeas/article/view/5076/pdf_35
_version_ 1772816020959920128
score 13.211869

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