(1) Department of Mechanical Engineering, Faculty of Sciences and Applied Sciences, University of Bouira, Bouira 10000, Algeria. 2) Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, urban pole, 26000, Medea, Algeria)(2) Yamina Ammi
(University of Medea, Algeria)(3) Kouidri Ikram
(University of Relizane, Algeria)(4) Sofiane Kherrour
(Centre for the Development of Renewable Energies, Algeria)(5) Salah Hanini
(University of Medea, Algeria)(6) Raheem Al-Sabur
(University of Basrah, Iraq)(7) Maamar Laidi
(University of Medea, Algeria)(8) Alfian Ma’arif
(1) Department of Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta 55191, Indonesia. 2) Department of Engineering Profession, Universitas Muhammadiyah Yogyakarta, Yogyakarta 55183, Indonesia)(9) * Abdel-Nasser Sharkawy
(1) Mechanical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt. 2) Mechanical Engineering Department, College of Engineering, Fahad Bin Sultan University, Tabuk 47721, Saudi Arabia)*corresponding author
AbstractIn the rapidly developing field of smart cities, accurately predicting weather conditions plays a vital role in various sectors, including industry, tourism, agriculture, social planning, architecture, and economic development. Unfortunately, the instruments used (such as pyranometers, barometers, and thermometers) often suffer from low accuracy, high computational costs, and a lack of robustness. This limitation affects the reliability of weather predictions and their application across these critical areas. This study proposes artificial neural network-multilayer perceptrons (ANN-MLPs). A dataset of 480 data points was used, with 80% allocated for the training phase, 10% for the validation phase, and 10% for the testing phase. The best results were obtained with the structure 6-17-1 (6 inputs, 17 hidden neurons, and 1 output neuron) to predict weather condition data in the Ghardaïa district. Weather conditions parameters include air temperature, relative humidity, wind speed, and cumulative precipitation. Results showed that the most relevant input factors are, in order of importance: earth-sun distance (DT-S) with a relative importance (RI) of 31.10%, factor conversion (d) with an RI of 26.05%, and solar radiation (SR) with an RI of 16.26%. The contribution of the elevation of the sun (HI) has an RI of 13.29%. The optimal configuration includes seventeen neurons in the hidden layer with a logistic sigmoid activation function and a Levenberg–Marquardt learning algorithm, resulting in a root mean square error (RMSE) of 3.3043% and a correlation coefficient (R) of 0.9683. The proposed model can predict both short- and long-term climate factors such as solar radiation, air temperature, and wind energy in areas with similar conditions. KeywordsGhardaïa District; Earth-Sun Distance; Weather Prediction Model; Solar Radiation; Machine Learning
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DOIhttps://doi.org/10.31763/ijrcs.v5i1.1651 |
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References
[1] L. Chen et al., “FuXi: a cascade machine learning forecasting system for 15-day global weather forecast,†npj climate and atmospheric science, vol. 6, no. 1, pp. 1–11, 2023, https://doi.org/10.1038/s41612-023-00512-1.
[2] M. T. Hasan, K. M. Fattahul Islam, M. S. Rahman, and S. Li, “Weather Forecasting Using Artificial Neural Network,†Artificial Intelligence and Security, vol. 11633, no. 22, pp. 171–180, 2019, https://doi.org/10.1007/978-3-030-24265-7_15.
[3] M. Laidi, S. Hanini, A. Rezrazi, M. R. Yaiche, A. A. El Hadj, and F. Chellali, “Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria),†Theoretical and Applied Climatology, vol. 128, no. 1-2, pp. 439-451, 2016, https://doi.org/10.1007/s00704-015-1720-7.
[4] A. Dahmani, Y. Ammi, and S. Hanini, “A Novel Non-Linear Model Based on Bootstrapped Aggregated Support Vector Machine for the Prediction of Hourly Global Solar Radiation,†Smart Grids and Sustainable Energy, vol. 9, no. 1, p. 3, 2023, https://doi.org/10.1007/s40866-023-00179-w.
[5] S. Scher and G. Messori, “Predicting weather forecast uncertainty with machine learning,†Quarterly Journal of the Royal Meteorological Society, vol. 144, no. 717, pp. 2830–2841, 2018, https://doi.org/10.1002/qj.3410.
[6] A. -N. Sharkawy et al., "Solar PV Power Estimation and Upscaling Forecast Using Different Artificial Neural Networks Types: Assessment, Validation, and Comparison," IEEE Access, vol. 11, pp. 19279-19300, 2023, https://doi.org/10.1109/ACCESS.2023.3249108.
[7] J. Sillmann et al., “Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities,†Weather and Climate Extremes, vol. 18, pp. 65–74, 2017, https://doi.org/10.1016/j.wace.2017.10.003.
[8] Z. Pang, F. Niu, and Z. O’Neill, “Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons,†Renewable Energy, vol. 156, pp. 279–289, 2020, https://doi.org/10.1016/j.renene.2020.04.042.
[9] N. Bailek et al., “Developing a new model for predicting global solar radiation on a horizontal surface located in Southwest Region of Algeria,†NRIAG Journal of Astronomy and Geophysics, vol. 9, no. 1, pp. 341–349, 2020, https://doi.org/10.1080/20909977.2020.1746892.
[10] B. Amiri, R. Dizène, and K. Dahmani, “Most relevant input parameters selection for 10-min global solar irradiation estimation on arbitrary inclined plane using neural networks,†International Journal of Sustainable Energy, vol. 39, no. 8, pp. 779–803, 2020, https://doi.org/10.1080/14786451.2020.1758104.
[11] A. Dahmani et al., “Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction,†Computers, Materials & Continua, vol. 77, no. 2, pp. 2579-2594, 2023, https://doi.org/10.32604/cmc.2023.040625.
[12] A. Sharkawy, M. M. Ali, H. H. H. Mousa, A. S. Ali, G. T. Abdel-Jaber, “Short-Term Solar PV Power Generation Day-Ahead Forecasting,†International Journal of Robotics and Control Systems, vol. 2, no. 3, pp. 562–580, 2022, https://doi.org/10.31763/ijrcs.v2i3.780.
[13] M. Afzali, A. Afzali, and G. Zahedi, “The Potential of Artificial Neural Network Technique in Daily and Monthly Ambient Air Temperature Prediction,†International Journal of Environmental Science and Development, vol. 3, no. 1, pp. 33–38, 2012, https://doi.org/10.7763/IJESD.2012.V3.183.
[14] T. T. K. Tran, S. M. Bateni, S. J. Ki, and H. Vosoughifar, “A review of neural networks for air temperature forecasting,†Water, vol. 13, no. 9, p. 1294, 2021, https://doi.org/10.3390/w13091294.
[15] S. AlSadi and T. Khatib, “Modeling of relative humidity using artificial neural network,†Journal of Asian Scientific Research, vol. 2, no. 2, p. 81, 2012, https://archive.aessweb.com/index.php/5003/article/view/3329.
[16] E. Kuzugudenli, “Relative humidity modeling with artificial neural networks,†Applied Ecology and Environmental Research, vol. 16, no. 4, pp. 5227–5235, 2018, https://www.aloki.hu/pdf/1604_52275235.pdf.
[17] M. I. C. Rachmatullah, J. Santoso, and K. Surendro, “Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction,†PeerJ Computer Science, vol. 7, p. e724, 2021, https://doi.org/10.7717/peerj-cs.724.
[18] A. N. Sharkawy, A. G. Ameen, S. Mohamed, G. T. Abdel-Jaber, and I. Hamdan, “Design, Assessment, and Modeling of Multi-Input Single-Output Neural Network Types for the Output Power Estimation in Wind Turbine Farms,†Automation, vol. 5, no. 2, pp. 190–212, 2024, https://doi.org/10.3390/automation5020012.
[19] Z. Tang, G. Zhao, and T. Ouyang, “Two-phase deep learning model for short-term wind direction forecasting,†Renewable Energy, vol. 173, pp. 1005–1016, 2021, https://doi.org/10.1016/j.renene.2021.04.041.
[20] A. Bouach, “Artificial neural networks for monthly precipitation prediction in north-west Algeria: a case study in the Oranie-Chott-Chergui basin,†Journal of Water & Climate Change, vol. 15, no. 2, pp. 582–592, 2024, https://doi.org/10.2166/wcc.2024.494.
[21] S. Sharadqah, A. M. Mansour, M. A. Obeidat, R. Marbello, and S. M. Perez, “Nonlinear Rainfall Yearly Prediction based on Autoregressive Artificial Neural Networks Model in Central Jordan using Data Records: 1938-2018,†International Journal of Advanced Computer Science and Applications (IJACSA), vol. 12, no. 2, pp. 240-247, 2021, https://dx.doi.org/10.14569/IJACSA.2021.0120231.
[22] M. Ghamariadyan and M. A. Imteaz, “Prediction of seasonal rainfall with one-year lead time using climate indices: A wavelet neural network scheme,†Water Resources Management, vol. 35, no. 15, pp. 5347–5365, 2021, https://doi.org/10.1007/s11269-021-03007-x.
[23] K. Abhishek, M. P. Singh, S. Ghosh, and A. Anand, “Weather forecasting model using artificial neural network,†Procedia Technology, vol. 4, pp. 311–318, 2012, https://doi.org/10.1016/j.protcy.2012.05.047.
[24] R. Yacef, A. Mellit, S. Belaid, and Z. Şen, “New combined models for estimating daily global solar radiation from measured air temperature in semi-arid climates: application in Ghardaïa, Algeria,†Energy Conversion and Management, vol. 79, pp. 606–615, 2014, https://doi.org/10.1016/j.enconman.2013.12.057.
[25] M. Guermoui, S. Benkaciali, K. Gairaa, K. Bouchouicha, T. Boulmaiz, and J. W. Boland, “A novel ensemble learning approach for hourly global solar radiation forecasting,†Neural Computing and Applications, vol. 34, no. 4, pp. 2983–3005, 2022, https://doi.org/10.1007/s00521-021-06421-9.
[26] H. Bouzemlal et al., “New method of prediction of climatic conditions by artificial neural networks (case of the province of Médéa),†Algerian Journal of Environmental Science and Technology, vol. 9, no. 4, pp. 1419-1428, 2023, https://www.aljest.net/index.php/aljest/article/view/912.
[27] R. W. Mueller, “Solar irradiance, global distribution,†Solar Energy, pp. 553–583, 2013, https://doi.org/10.1007/978-1-4614-5806-7_447.
[28] J. A. Duffie and W. A. Beckman, “Solar engineering of thermal processes,†John Wiley & Sons, 2013, https://doi.org/10.1002/9781118671603.
[29] B. Amiri, “Estimation temporelle du rayonnement solaire au nord et au Sahara Algérien à partir de données de mesures sélectionnées,†PhD Thesis, Université des Sciences et de la Technologie d'Alger, 2021, https://dspace.usthb.dz/handle/123456789/9228.
[30] D. Markovics, M. J. Mayer, “Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction,†Renewable and Sustainable Energy Reviews, vol. 161, p. 112364, 2022, https://doi.org/10.1016/j.rser.2022.112364.
[31] Z. Şen, “Solar Radiation Deterministic Models,†Solar Energy Fundamentals and Modeling Techniques, pp. 47-99, 2008, https://doi.org/10.1007/978-1-84800-134-3_3.
[32] R. Foster, M. Ghassemi, and A. Cota, “Solar energy: renewable energy and the environment,†CRC press, p. 382, 2009, https://doi.org/10.1201/9781420075670.
[33] C. M. Siham, H. Salah, L. Maamar, and K. Latifa, “Artificial neural networks based prediction of hourly horizontal solar radiation data: case study,†International Journal of Applied Decision Sciences, vol. 10, no. 2, p. 156, 2017, https://doi.org/10.1504/IJADS.2017.084312.
[34] A. Dahmani, Y. Ammi, and S. Hanini, “Neural network for prediction solar radiation in Relizane region (Algeria)-Analysis study,†International Journal of Energetica, vol. 7, no. 2, pp. 8–18, 2022, https://www.ajol.info/index.php/ijeca/article/view/264400.
[35] M. A. Hassan, M. Abubakr, A. Khalil, “A profile-free non-parametric approach towards generation of synthetic hourly global solar irradiation data from daily totals,†Renewable Energy, vol. 167, pp. 613-628, 2021, https://doi.org/10.1016/j.renene.2020.11.125.
[36] Y. Ammi, L. Khaouane, and S. Hanini, “A Model Based on Bootstrapped Neural Networks for Modeling the Removal of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes,†Arabian Journal for Science and Engineering, vol. 43, no. 11, pp. 6271–6284, 2018, https://doi.org/10.1007/s13369-018-3484-8.
[37] A. Dahmani, Y. Ammi, S. Hanini, and Z. Driss, “Developed nonlinear model based on bootstrap aggregated neural networks for predicting global hourly scale horizontal irradiance,†International Journal of Energetica, vol. 8, no. 1, pp. 31–37, 2023, https://www.ajol.info/index.php/ijeca/article/view/263636.
[38] A. Dahmani, Y. Ammi, S. Hanini, M. Redha Yaiche, and H. Zentou, “Prediction of Hourly Global Solar Radiation: Comparison of Neural Networks/Bootstrap Aggregating,†Kemija u industriji: ÄŒasopis kemiÄara i kemijskih inženjera Hrvatske, vol. 72, no. 3–4, pp. 201–213, 2023, https://doi.org/10.15255/KUI.2022.065.
[39] A.-N. Sharkawy, M. Ali, H. Mousa, A. Ali, and G. Abdel-Jaber, “Machine Learning Method for Solar PV Output Power Prediction,†SVU-International Journal of Engineering Sciences and Applications, vol. 3, no. 2, pp. 123–130, 2022, https://doi.org/10.21608/svusrc.2022.157039.1066.
[40] A.-N. Sharkawy, “Principle of Neural Network and Its Main Types: Review,†Journal of Advances in Applied & Computational Mathematics, vol. 7, pp. 8–19, 2020, https://doi.org/10.15377/2409-5761.2020.07.2.
[41] Y. Ammi, L. Khaouane, and S. Hanini, “Stacked neural networks for predicting the membranes performance by treating the pharmaceutical active compounds,†Neural Computing and Applications, vol. 33, no. 19, pp. 12429–12444, 2021, https://doi.org/10.1007/s00521-021-05876-0.
[42] K. Ikram, K. Djilali, D. Abdennasser, R. Al-Sabur, B. Ahmed, and A. N. Sharkawy, “Comparative analysis of fouling resistance prediction in shell and tube heat exchangers using advanced machine learning techniques,†Research on Engineering Structures and Materials, vol. 10, no. 1, pp. 253–270, 2024, http://dx.doi.org/10.17515/resm2023.858en0816.
[43] N. Bailek et al., “A new empirical model for forecasting the diffuse solar radiation over Sahara in the Algerian Big South,†Renewable Energy, vol. 117, pp. 530–537, 2018, https://doi.org/10.1016/j.renene.2017.10.081.
[44] A. Benyekhlef, B. Mohammedi, S. Hanini, M. Boumahdi, A. Rezrazi, and M. Laidi, “A Contribution to the Modelling of Fouling Resistance in Heat Exchanger-Condenser by Direct and Inverse Artificial Neural Network,†Kemija u industriji: ÄŒasopis kemiÄara i kemijskih inženjera Hrvatske, vol. 70, no. 11–12, pp. 639–650, 2021, https://doi.org/10.15255/KUI.2020.076.
[45] J. KoÄÃ, V. KoÄÃ, J. MadÄ›ra, R. ÄŒerný, “Effect of applied weather data sets in simulation of building energy demands: Comparison of design years with recent weather data,†Renewable and Sustainable Energy Reviews, vol. 100, pp. 22-32, 2019, https://doi.org/10.1016/j.rser.2018.10.022.
[46] M. R. Fissa, Y. Lahiouel, L. Khaouane, and S. Hanini, “QSPR estimation models of normal boiling point and relative liquid density of pure hydrocarbons using MLR and MLP-ANN methods,†Journal of Molecular Graphics and Modelling, vol. 87, pp. 109–120, 2019, https://doi.org/10.1016/j.jmgm.2018.11.013.
[47] A. T. C. Goh, “Back-propagation neural networks for modeling complex systems,†Artificial Intelligence in Engineering, vol. 9, no. 3, pp. 143–151, 1995, https://doi.org/10.1016/0954-1810(94)00011-S.
[48] M. Mansourian, L. Saghaie, A. Fassihi, A. Madadkar-Sobhani, and K. Mahnam, “Linear and nonlinear QSAR modeling of 1, 3, 8-substituted-9-deazaxanthines as potential selective A 2B AR antagonists,†Medicinal Chemistry Research, vol. 22, pp. 4549–4567, 2013, doi: https://doi.org/10.1007/s00044-012-0453-8.
[49] M. Hamadache, O. Benkortbi, S. Hanini, and A. Amrane, “QSAR modeling in ecotoxicological risk assessment: application to the prediction of acute contact toxicity of pesticides on bees (Apis mellifera L.),†Environmental Science and Pollution Research, vol. 25, pp. 896–907, 2018, https://doi.org/10.1007/s11356-017-0498-9.
[50] I. Euldji, A. Belghait, C. Si-Moussa, O. Benkortbi, and A. Amrane, “A new hybrid quantitative structure property relationships-support vector regression (QSPR-SVR) approach for predicting the solubility of drug compounds in supercritical carbon dioxide,†AIChE Journal, vol. 69, no. 8, p. e18115, 2023, https://doi.org/10.1002/aic.18115.
[51] Y. Ammi, C. Si-Moussa, and S. Hanini, “Machine Learning and Neural Networks for Modelling the Retention of PPhACs by NF/RO,†Kemija u industriji: ÄŒasopis kemiÄara i kemijskih inženjera Hrvatske, vol. 72, no. 11–12, pp. 617–626, 2023, https://doi.org/10.15255/KUI.2022.085.
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