Development and Assessment of Feed Forward Back Propagation Neural Network Models to Predict Sunshine Duration

Main Article Content

Berivan H. Mahdi
https://orcid.org/0000-0003-1538-9533
Jwan A. Mohammed
https://orcid.org/0000-0002-5174-7521
Amera I. Melhum
https://orcid.org/0000-0001-7269-8637

Abstract

         The duration of sunshine is one of the important indicators and one of the variables for measuring the amount of solar radiation collected in a particular area. Duration of solar brightness has been used to study atmospheric energy balance, sustainable development, ecosystem evolution and climate change. Predicting the average values of sunshine duration (SD) for Duhok city, Iraq on a daily basis using the approach of artificial neural network (ANN) is the focus of this paper. Many different ANN models with different input variables were used in the prediction processes. The daily average of the month, average temperature, maximum temperature, minimum temperature, relative humidity, wind direction, cloud level and atmospheric pressure were used as input parameters in order to obtain the daily average of sunshine duration (SD) as the output. The eight-year data were divided into two categories. The first category covers whole years (annually) and the second category is seasonal. To recognize and assess the influence of different input parameters on sunshine duration, six models of ANN have been evolved. The findings showed that in the annual models, the outcomes of RMSE, MAE and R for the model with input parameters (Month, Cloud Level and Average Temperature) were the best results 1.82, 1.175 and 0.89, respectively. As for the season models, the outcomes of RMSE, MAE and R for the autumn season were the best results 1.450, 1.009 and 0.94, respectively. Accordingly, the performance of the artificial neural network is considerably effective in predicting the sunshine duration.

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Mahdi BH, Jwan A. Mohammed, Amera I. Melhum. Development and Assessment of Feed Forward Back Propagation Neural Network Models to Predict Sunshine Duration. IJP [Internet]. 2022 Sep. 1 [cited 2023 Feb. 6];20(3):64-75. Available from: https://ijp.uobaghdad.edu.iq/index.php/physics/article/view/1015
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References

Angstrom A., Solar and Terrestrial Radiation. Quarterly Journal of the Royal Meteorological Society, 1924 .50(210): pp.121–125.

Ahmadi H. and Ahmadi F., Evaluation of Sunshine Duration and Temporal–Spatial Distribution Based on Geo-statistical Methods in Iran. Int. J. Environ. Sci. Technol., 2019. 16(3-4): pp.1589–1602.

Abakumova G. M., Feigelson E. M., Russak V. and Stadnik V.V., Evaluation of Long-Term Changes in Radiation, Cloudiness, and Surface Temperature on The Territory of The Former Soviet Union. Journal of Climate, 1996. 9(6): pp.1319–1327.

Sozen A., Arcaklioglu E. and Ozalp M., Estimation of Solar Potential in Turkey by Artificial Neural Networks Using Meteorological and Geographical Data. Energy Conversion and Management, 2004. 45(18-19): pp.3033–3052.

Ozyildirim B. M. and Avci M., Generalized Classifier Neural Network. Neural Networks, 2013.39: pp.18–26.

Sanchez-Lorenzo A., Calb´o J., Brunetti M. and Deser C., Dimming/brightening over The Iberian Peninsula: Trends in Sunshine Duration and Cloud Cover and their Relations with Atmospheric Circulation. Journal of Geophysical Research, 2009. 114(D10): pp.1-17.

Ouammi A., Zejli D., Dagdougui H. and Benchrifa R., Artificial Neural Network Analysis of Moroccan Solar Potential. Renewable & Sustainable Energy Reviews, 2012. 16(7): pp.4876– 4889.

Rahimikhoob A., Behbahani S. M. R. and Banihabib M. E., Comparative Study of Statistical and Artificial Neural Network’s Methodologies for Deriving Global Solar Radiation from NOAA Satellite Images. International Journal of Climatology, 2013. 33(2): pp.480–486.

East B., Mean Annual Hours of Sunshine and the Incidence Dental Caries. American Journal of Public Health, 1939. 29(7): pp.777–780.

Suehrcke H., On the Relationship Between Duration of Sunshine and solar Radiation on The Earth’s Surface: Angstr¨om’s Equation Revisited. Solar Energy, 2000 68(5): pp.417–425.

Mahdi B. H., Yousif K. M. and Dosky L. MS., Using Artificial Neural Networks to Predict Solar Radiation for Duhok City, Iraq. 2020 International Conference on Computer Science and Software Engineering (CSASE). Duhok, Kurdistan, Iraq, 2020. IEEE. pp.1-6.

Suehrcke H., Bowden R. S., and Hollands K.G.T., Relationship between Sunshine Duration and Solar Radiation. Solar Energy, 2013. 92: pp.160–171.

Rocha P.A.C., Fernandes J.L., Modolo A.B., Rima R.J., Silva M.E. and Bezerra C.A., Estimation of Daily, Weekly and Monthly Global Solar Radiation Using ANNs and a Long Data Set: A Case Study of Fortaleza, in Brazilian Northeast region. Int J Energy Environ Eng, 2019. 10: pp.319–334.

Widodo D. A., Purwanto P. and Hermawan H., Modeling Solar Potential in Semarang, Indonesia Using Artificial Neural Networks. Journal of Applied Engineering Science, 2021. 19(3): pp.578-585.

Jervase J. A., Al-Lawati A., and Dorvlo A. S. S., Contour Maps for Sunshine Ratio for Oman Using Radial Basis Function Generated Data. Renewable Energy, 2003. 28(3): pp.487–497.

Mohandes, Rehman M., S., and Halawani T. O., Estimation of Global Solar Radiation Using Artificial Neural Networks. Renewable Energy,1998.14(1–4): pp.179–184.

Goni S., Adannou H.A., Diop D., Drame M.S., Tikri B., Barka M., and, Beye A.C., Long-Term Variation of Sunshine Duration and Their Interaction with Metrological Parameters over Chad, Central Africa. Natural Resources, 2019. 10(3): pp.47-58.

Zhu W., Wu B., Ma N., Yan Z., Liu L., Wang W., Xing Q. and Xu J., Estimating Sunshine Duration Using Hourly Total Cloud Amount Data from a Geostationary Meteorological Satellite. Atmosphere, 2020. 11(1): pp.1-17.

El-kenawy ES.M., Ibrahim A., Bailek N., Bouchouicha K., Hassan M.A., Jamei M. and Al-Ansari N., Sunshine Duration Measurements and Predictions in Saharan Algeria Region: an Improved Ensemble learning Approach. Theoretical and Applied Climatology, 2022. 147(3): pp.1015–1031.

Mahdi B. H., Yousif K. M. and Dosky L. MS., Influence of Meteorological Parameters on Air Quality and other Pollutants in Duhok City, Iraq. Iraqi Journal of Agricultural Sciences, 2020. 51(4): pp.1160-1172.

Rahimikhoob A., Estimating Sunshine Duration from other Climatic Data by Artificial Neural Network for ET0 Estimation in an Arid Environment. Theoretical and Applied Climatology. Wien, 2014. 118(1): pp.1-8.

Mohamed Z.E., Using the Artificial Neural Networks for Prediction and Validating Solar Radiation. Journal of the Egyptian Mathematical Society, 2019. 27(1-2): pp.1-13.

Marzouq M., Bounoua, Z., Mechaqrane A., el fadili H., Lakhliai Z., Zenkouar K., ANN-based Modelling and Prediction of Daily Global Solar Irradiation Using Commonly Measured Meteorological Parameters. IOP Conference Series: Earth and Environmental Science, 2018. 161(1): pp.012017(1-6).

Lu N., Qin J., Yang K., and Sun J., A Simple and Efficient Algorithm to Estimate Daily Global Solar Radiation Fromgeo Stationary Satellite Data. Energy, 2011. 36(5): pp.3179–3188.

Rojas M.G., Olivera A.C. and Vidal P.J., Optimizing Multilayer Perceptron Weights and Biases through a Cellular Genetic Algorithm for medical data classification. Array, 2022. 14(1):p.100173-(1-15).

Alghamdi H.A., A Time Series Forecasting of Global Horizontal Irradiance on Geographical Data of Najran Saudi Arabia. Energies, 2022. 15(3): p. 1-19

Ozkan F., Comparing the Forecasting Performance of Neural Network and Purchasing Power Parity: The Case of Turkey. Economic Modeling, 2013. 31(1): pp.752–758.

Mohammed, J. A. and Mahi., The Prediction of Solar Radiation Using Fuzzy Logic: A Case Study. Journal of University of Duhok: Pure and Engineering Sciences, 2018. 21(2): pp.34-44.

Choosri P., Foobunma K., Kongsomlit A., A Method to Estimation of Global Solar Radiation with Meteorological Parameters under Cloudless Sky Condition using Artificial Neural Network. Naresuan University Journal: Science and technology (nujst), 2021. 29(4): p. 1-12.

Melhum A. I. and Mohammed J. A., Adaptive Neuro-Fuzzy Inference System for Estimation of Water Quality Index in Duhok Camps. Journal of Duhok University: Pure and Engineering Sciences, 2019. 22(1): pp.113-123.