Modelling the Solar Intensity of Asaba Town in Nigeria Using Response Surface Methodology and Machine Learning Techniques

Akpenyi-Aboh Onyekachukwu Nicklette; Okafor Obiora Clement

doi:doi:10.11648/j.ajmie.20240904.11

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Modelling the Solar Intensity of Asaba Town in Nigeria Using Response Surface Methodology and Machine Learning Techniques

Akpenyi-Aboh Onyekachukwu Nicklette^*

, Okafor Obiora Clement

Published in American Journal of Mechanical and Industrial Engineering (Volume 9, Issue 4)

Received: 1 August 2024 Accepted: 20 September 2024 Published: 31 October 2024

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Abstract

The use of solar energy devices and machines such as solar pumps, heaters, solar cars, solar air conditioners, solar refrigerators, etc. has been the trend in most developed countries in the world owing to the green nature of solar technology. Based on the vast areas of application of solar technology, the knowledge of the solar intensity of a geographical location and having an estimation model to reduce the frequent measurement of solar-dependent factors are very imperative and helpful to the designers of solar devices and machines. This concern necessitated this research that was centered on determining the optimal value of the solar intensity of Asaba town, Delta state, and developing a mathematical model for estimating the solar intensity of the region. Meteorological data of the region was collected from the Nigerian Meteorological Agency (NIMET) for ten years (2011-2020). The data obtained from NIMET were: rainfall amount, relative humidity, mean temperature, and solar intensity. A multiple linear regression (MLR) model was developed using the collected data and the artificial neural network (ANN) model was also used to estimate solar intensity of the region. Response surface methodology (RSM) tool was employed to perform numerical optimization using the collected data of the region in quest of getting an optimal value of the solar intensity of Asaba town and the combinatorial best factor levels- rainfall, relative humidity, and mean temperature that would yield the optimum value of the response variable- solar intensity. Statistical tools such as mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), Nash-Sutcliffe equation (NSE), correlation coefficient, test- statistics, and coefficient of determination (R²) were used to evaluate the estimation performance of the Quartic polynomial model developed from the optimization process, multiple linear regression, and the artificial neural network models. From the results obtained, the optimal value of solar intensity was 759.687w/m² at the factor levels of: rainfall-194.58 mm, relative humidity-28.7989 mmHg and mean temperature of 25.7288°C. Also, the statistical validation tools applied revealed that the Quartic polynomial model had a much better performance characteristic than the other estimation models. This study would find application in the field of heating, ventilation, and air-conditioning (HVAC) solar systems by aiding the designers of such systems in knowing and estimating the value of the solar intensity of Asaba and other geographical regions that have similar climatic conditions.

Published in	American Journal of Mechanical and Industrial Engineering (Volume 9, Issue 4)
DOI	10.11648/j.ajmie.20240904.11
Page(s)	63-74
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Solar Intensity, Response Surface Methodology, Artificial Neural Network, Least-square Regression Analysis, HVAC Systems

References

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[2]	Banjade D., Poudyal K. N., Daponte P., Rapuano S. and Vito L. di, (2010), Estimation of Global Solar Radiation using Empirical Models at Benevento, Italy, National Seminar on Power and Communication Sectors Development (PCSD), Kathmandu, Nepal, 41-44.
[3]	Abdulrahim, A. T., Diso, I. S., El-Jummah, A. M. (2011). Solar concentrators’ developments in Nigeria: A review. Continental Journal of Engineering Sciences, Vol. 6(3), pp. 30-37.
[4]	Nnabuenyi, H. O., Okoli, L. N., Nwosu, F. C., and Ibe, G., (2017). Estimation of global solar radiation using sunshine and temperature-based models for Oko town in Anambra State, Nigeria. American Journal of Renewable and Sustainable Energy, 3(2): 8-14.
[5]	Okundamiya, M. S., Emagbetere, J. O., & Ogujor, E. A. (2015). Evaluation of various global solar radiation models for Nigeria. International Journal of Green Energy, 13(5), 505–512. https://doi.org/10.1080/15435075.2014.968921
[6]	Ezemuo, D. T., Ogunoh, C. and AguhP. S., (2017): Photovoltaic Solar Radiation Systems Analysis. Journal of Scientific and Engineering Research, 2017, vol.4(9), pp 93-106.
[7]	Onyeka V. O., Nwobi-Okoye C. C., Okafor O. C., Madu K. E., Mbah O. M. (2021). Estimation of global solar radiation using empirical models. Journal of Engineering Sciences, 8(2), pp. 11-19.
[8]	Mbah, O. M., Madueke, C. I., Umunakwe, R., & Okafor, C. O. (2022). Machine Learning Approach for Solar Irradiance Estimation on Tilted Surfaces in Comparison with Sky Models Prediction. Journal of Engineering Sciences, 9(2), G1–G6. https://doi.org/10.21272/jes.2022.9(2).g1
[9]	Trabea, A. A., & Shaltout, M. A. M. (2000). Correlation of global solar radiation with meteorological parameters over Egypt. Renewable Energy, 21(2), 297–308. https://doi.org/10.1016/s0960-1481(99)00127-5
[10]	Awachie, I. R. N and Okeke, C. E. (1990). New empirical solar model and its use in predicting global solar irradiation. Nigerian Journal of Solar Energy, vol. 9, pp. 143-156.
[11]	Duffie, J. A., Beckman, W. A. (2013). Solar Engineering of Thermal Processes. John Wiley and Sons, New York, USA.
[12]	Robaa, S. M. (2008). Evaluation of sunshine duration from cloud data in Egypt. Energy, Vol. 33, pp. 785-795.
[13]	Glover, J., McCulloch, J. S. (1958). The empirical relation between solar radiation and hours of sunshine. Journal of Royal Meteorological Society, Vol. 84, pp. 172-175.
[14]	Chen, R., Ersi, K., Yang, J., Lu, S., Zhao, W. (2004). Validation of five global radiation models with measured daily data in China. Energy Conversion and Management, Vol. 45, pp. 1759-1769.

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APA Style

Nicklette, A. O., Clement, O. O. (2024). Modelling the Solar Intensity of Asaba Town in Nigeria Using Response Surface Methodology and Machine Learning Techniques. American Journal of Mechanical and Industrial Engineering, 9(4), 63-74. https://doi.org/10.11648/j.ajmie.20240904.11

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ACS Style

Nicklette, A. O.; Clement, O. O. Modelling the Solar Intensity of Asaba Town in Nigeria Using Response Surface Methodology and Machine Learning Techniques. Am. J. Mech. Ind. Eng. 2024, 9(4), 63-74. doi: 10.11648/j.ajmie.20240904.11

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AMA Style

Nicklette AO, Clement OO. Modelling the Solar Intensity of Asaba Town in Nigeria Using Response Surface Methodology and Machine Learning Techniques. Am J Mech Ind Eng. 2024;9(4):63-74. doi: 10.11648/j.ajmie.20240904.11

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@article{10.11648/j.ajmie.20240904.11,
  author = {Akpenyi-Aboh Onyekachukwu Nicklette and Okafor Obiora Clement},
  title = {Modelling the Solar Intensity of Asaba Town in Nigeria Using Response Surface Methodology and Machine Learning Techniques
},
  journal = {American Journal of Mechanical and Industrial Engineering},
  volume = {9},
  number = {4},
  pages = {63-74},
  doi = {10.11648/j.ajmie.20240904.11},
  url = {https://doi.org/10.11648/j.ajmie.20240904.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmie.20240904.11},
  abstract = {The use of solar energy devices and machines such as solar pumps, heaters, solar cars, solar air conditioners, solar refrigerators, etc. has been the trend in most developed countries in the world owing to the green nature of solar technology. Based on the vast areas of application of solar technology, the knowledge of the solar intensity of a geographical location and having an estimation model to reduce the frequent measurement of solar-dependent factors are very imperative and helpful to the designers of solar devices and machines. This concern necessitated this research that was centered on determining the optimal value of the solar intensity of Asaba town, Delta state, and developing a mathematical model for estimating the solar intensity of the region. Meteorological data of the region was collected from the Nigerian Meteorological Agency (NIMET) for ten years (2011-2020). The data obtained from NIMET were: rainfall amount, relative humidity, mean temperature, and solar intensity. A multiple linear regression (MLR) model was developed using the collected data and the artificial neural network (ANN) model was also used to estimate solar intensity of the region. Response surface methodology (RSM) tool was employed to perform numerical optimization using the collected data of the region in quest of getting an optimal value of the solar intensity of Asaba town and the combinatorial best factor levels- rainfall, relative humidity, and mean temperature that would yield the optimum value of the response variable- solar intensity. Statistical tools such as mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), Nash-Sutcliffe equation (NSE), correlation coefficient, test- statistics, and coefficient of determination (R2) were used to evaluate the estimation performance of the Quartic polynomial model developed from the optimization process, multiple linear regression, and the artificial neural network models. From the results obtained, the optimal value of solar intensity was 759.687w/m2 at the factor levels of: rainfall-194.58 mm, relative humidity-28.7989 mmHg and mean temperature of 25.7288°C. Also, the statistical validation tools applied revealed that the Quartic polynomial model had a much better performance characteristic than the other estimation models. This study would find application in the field of heating, ventilation, and air-conditioning (HVAC) solar systems by aiding the designers of such systems in knowing and estimating the value of the solar intensity of Asaba and other geographical regions that have similar climatic conditions.
},
 year = {2024}
}

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TY  - JOUR
T1  - Modelling the Solar Intensity of Asaba Town in Nigeria Using Response Surface Methodology and Machine Learning Techniques

AU  - Akpenyi-Aboh Onyekachukwu Nicklette
AU  - Okafor Obiora Clement
Y1  - 2024/10/31
PY  - 2024
N1  - https://doi.org/10.11648/j.ajmie.20240904.11
DO  - 10.11648/j.ajmie.20240904.11
T2  - American Journal of Mechanical and Industrial Engineering
JF  - American Journal of Mechanical and Industrial Engineering
JO  - American Journal of Mechanical and Industrial Engineering
SP  - 63
EP  - 74
PB  - Science Publishing Group
SN  - 2575-6060
UR  - https://doi.org/10.11648/j.ajmie.20240904.11
AB  - The use of solar energy devices and machines such as solar pumps, heaters, solar cars, solar air conditioners, solar refrigerators, etc. has been the trend in most developed countries in the world owing to the green nature of solar technology. Based on the vast areas of application of solar technology, the knowledge of the solar intensity of a geographical location and having an estimation model to reduce the frequent measurement of solar-dependent factors are very imperative and helpful to the designers of solar devices and machines. This concern necessitated this research that was centered on determining the optimal value of the solar intensity of Asaba town, Delta state, and developing a mathematical model for estimating the solar intensity of the region. Meteorological data of the region was collected from the Nigerian Meteorological Agency (NIMET) for ten years (2011-2020). The data obtained from NIMET were: rainfall amount, relative humidity, mean temperature, and solar intensity. A multiple linear regression (MLR) model was developed using the collected data and the artificial neural network (ANN) model was also used to estimate solar intensity of the region. Response surface methodology (RSM) tool was employed to perform numerical optimization using the collected data of the region in quest of getting an optimal value of the solar intensity of Asaba town and the combinatorial best factor levels- rainfall, relative humidity, and mean temperature that would yield the optimum value of the response variable- solar intensity. Statistical tools such as mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), Nash-Sutcliffe equation (NSE), correlation coefficient, test- statistics, and coefficient of determination (R2) were used to evaluate the estimation performance of the Quartic polynomial model developed from the optimization process, multiple linear regression, and the artificial neural network models. From the results obtained, the optimal value of solar intensity was 759.687w/m2 at the factor levels of: rainfall-194.58 mm, relative humidity-28.7989 mmHg and mean temperature of 25.7288°C. Also, the statistical validation tools applied revealed that the Quartic polynomial model had a much better performance characteristic than the other estimation models. This study would find application in the field of heating, ventilation, and air-conditioning (HVAC) solar systems by aiding the designers of such systems in knowing and estimating the value of the solar intensity of Asaba and other geographical regions that have similar climatic conditions.

VL  - 9
IS  - 4
ER  -

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Author Information

Akpenyi-Aboh Onyekachukwu Nicklette

Mechanical Engineering Department, Delta State University of Science and Technology, Ozoro, Nigeria

Contact Email

http://orcid.org/0009-0000-6474-8518
Okafor Obiora Clement

Mechanical Engineering Department, Grundtvig Polytechnic, Oba, Nigeria

Contact Email

http://orcid.org/0000-0002-4900-6580

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Plain Text BibTeX RIS

APA Style

Nicklette, A. O., Clement, O. O. (2024). Modelling the Solar Intensity of Asaba Town in Nigeria Using Response Surface Methodology and Machine Learning Techniques. American Journal of Mechanical and Industrial Engineering, 9(4), 63-74. https://doi.org/10.11648/j.ajmie.20240904.11

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ACS Style

Nicklette, A. O.; Clement, O. O. Modelling the Solar Intensity of Asaba Town in Nigeria Using Response Surface Methodology and Machine Learning Techniques. Am. J. Mech. Ind. Eng. 2024, 9(4), 63-74. doi: 10.11648/j.ajmie.20240904.11

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AMA Style

Nicklette AO, Clement OO. Modelling the Solar Intensity of Asaba Town in Nigeria Using Response Surface Methodology and Machine Learning Techniques. Am J Mech Ind Eng. 2024;9(4):63-74. doi: 10.11648/j.ajmie.20240904.11

Copy | Download

@article{10.11648/j.ajmie.20240904.11,
  author = {Akpenyi-Aboh Onyekachukwu Nicklette and Okafor Obiora Clement},
  title = {Modelling the Solar Intensity of Asaba Town in Nigeria Using Response Surface Methodology and Machine Learning Techniques
},
  journal = {American Journal of Mechanical and Industrial Engineering},
  volume = {9},
  number = {4},
  pages = {63-74},
  doi = {10.11648/j.ajmie.20240904.11},
  url = {https://doi.org/10.11648/j.ajmie.20240904.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmie.20240904.11},
  abstract = {The use of solar energy devices and machines such as solar pumps, heaters, solar cars, solar air conditioners, solar refrigerators, etc. has been the trend in most developed countries in the world owing to the green nature of solar technology. Based on the vast areas of application of solar technology, the knowledge of the solar intensity of a geographical location and having an estimation model to reduce the frequent measurement of solar-dependent factors are very imperative and helpful to the designers of solar devices and machines. This concern necessitated this research that was centered on determining the optimal value of the solar intensity of Asaba town, Delta state, and developing a mathematical model for estimating the solar intensity of the region. Meteorological data of the region was collected from the Nigerian Meteorological Agency (NIMET) for ten years (2011-2020). The data obtained from NIMET were: rainfall amount, relative humidity, mean temperature, and solar intensity. A multiple linear regression (MLR) model was developed using the collected data and the artificial neural network (ANN) model was also used to estimate solar intensity of the region. Response surface methodology (RSM) tool was employed to perform numerical optimization using the collected data of the region in quest of getting an optimal value of the solar intensity of Asaba town and the combinatorial best factor levels- rainfall, relative humidity, and mean temperature that would yield the optimum value of the response variable- solar intensity. Statistical tools such as mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), Nash-Sutcliffe equation (NSE), correlation coefficient, test- statistics, and coefficient of determination (R2) were used to evaluate the estimation performance of the Quartic polynomial model developed from the optimization process, multiple linear regression, and the artificial neural network models. From the results obtained, the optimal value of solar intensity was 759.687w/m2 at the factor levels of: rainfall-194.58 mm, relative humidity-28.7989 mmHg and mean temperature of 25.7288°C. Also, the statistical validation tools applied revealed that the Quartic polynomial model had a much better performance characteristic than the other estimation models. This study would find application in the field of heating, ventilation, and air-conditioning (HVAC) solar systems by aiding the designers of such systems in knowing and estimating the value of the solar intensity of Asaba and other geographical regions that have similar climatic conditions.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Modelling the Solar Intensity of Asaba Town in Nigeria Using Response Surface Methodology and Machine Learning Techniques

AU  - Akpenyi-Aboh Onyekachukwu Nicklette
AU  - Okafor Obiora Clement
Y1  - 2024/10/31
PY  - 2024
N1  - https://doi.org/10.11648/j.ajmie.20240904.11
DO  - 10.11648/j.ajmie.20240904.11
T2  - American Journal of Mechanical and Industrial Engineering
JF  - American Journal of Mechanical and Industrial Engineering
JO  - American Journal of Mechanical and Industrial Engineering
SP  - 63
EP  - 74
PB  - Science Publishing Group
SN  - 2575-6060
UR  - https://doi.org/10.11648/j.ajmie.20240904.11
AB  - The use of solar energy devices and machines such as solar pumps, heaters, solar cars, solar air conditioners, solar refrigerators, etc. has been the trend in most developed countries in the world owing to the green nature of solar technology. Based on the vast areas of application of solar technology, the knowledge of the solar intensity of a geographical location and having an estimation model to reduce the frequent measurement of solar-dependent factors are very imperative and helpful to the designers of solar devices and machines. This concern necessitated this research that was centered on determining the optimal value of the solar intensity of Asaba town, Delta state, and developing a mathematical model for estimating the solar intensity of the region. Meteorological data of the region was collected from the Nigerian Meteorological Agency (NIMET) for ten years (2011-2020). The data obtained from NIMET were: rainfall amount, relative humidity, mean temperature, and solar intensity. A multiple linear regression (MLR) model was developed using the collected data and the artificial neural network (ANN) model was also used to estimate solar intensity of the region. Response surface methodology (RSM) tool was employed to perform numerical optimization using the collected data of the region in quest of getting an optimal value of the solar intensity of Asaba town and the combinatorial best factor levels- rainfall, relative humidity, and mean temperature that would yield the optimum value of the response variable- solar intensity. Statistical tools such as mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), Nash-Sutcliffe equation (NSE), correlation coefficient, test- statistics, and coefficient of determination (R2) were used to evaluate the estimation performance of the Quartic polynomial model developed from the optimization process, multiple linear regression, and the artificial neural network models. From the results obtained, the optimal value of solar intensity was 759.687w/m2 at the factor levels of: rainfall-194.58 mm, relative humidity-28.7989 mmHg and mean temperature of 25.7288°C. Also, the statistical validation tools applied revealed that the Quartic polynomial model had a much better performance characteristic than the other estimation models. This study would find application in the field of heating, ventilation, and air-conditioning (HVAC) solar systems by aiding the designers of such systems in knowing and estimating the value of the solar intensity of Asaba and other geographical regions that have similar climatic conditions.

VL  - 9
IS  - 4
ER  -

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