A Hybrid PSO-ANFIS Approach for Horizontal Solar Radiation Prediction in Nigeria
DOI:
https://doi.org/10.11113/elektrika.v18n2.153Keywords:
Solar radiation, PSO-ANFIS, GA-ANFIS, Prediction, NigeriaAbstract
For efficient and reliable hydrogen production via solar photovoltaic system, it is important to obtain accurate solar radiation data. Though there are equipment specifically designed for solar radiation prediction but are very expensive and have high maintenance cost that most countries like Nigeria are unable to purchase. In this study, the accuracy of a hybrid PSO-ANFIS method is examined to predict horizontal solar radiation in Nigeria. The prediction is done based on the available meteorological data obtained from NIMET Nigeria. The meteorological data used for this study are monthly mean minimum temperature, maximum temperature, relative humidity and sunshine hours, which serves as inputs to the developed model. The model accuracy is evaluated using two statistical indicators Root Mean Square Error (RMSE) and Coefficient of determination (R²). The accuracy of the proposed model is validated using ANFIS, GA-ANFIS models and other literatures. Based on the statistical parameters used for the model evaluation, the results obtained proves PSO-ANFIS as a good model for predicting solar radiation with the values of RMSE=0.68318, R²=0.9065 at the training stage and RMSE=1.3838, R²=0.8058 at the testing stage. This proves the potentiality of PSO-ANFIS technique for accurate solar radiation predictionReferences
A. Trabea and M. M. Shaltout, "Correlation of global solar radiation with meteorological parameters over Egypt," Renewable Energy, vol. 21, pp. 297-308, 2000.
K. Chiteka and C. Enweremadu, "Prediction of global horizontal solar irradiance in Zimbabwe using artificial neural networks," Journal of Cleaner Production, vol. 135, pp. 701-711, 2016.
T. N. Veziroglu, "21st Century's energy: hydrogen energy system," in Assessment of Hydrogen Energy for Sustainable Development, ed: Springer, 2007, pp. 9-31.
A. Contreras, R. Guirado, and T. Veziroglu, "Design and simulation of the power control system of a plant for the generation of hydrogen via electrolysis, using photovoltaic solar energy," International Journal of Hydrogen Energy, vol. 32, pp. 4635-4640, 2007.
M. Santarelli, M. Calı̀, and S. Macagno, "Design and analysis of stand-alone hydrogen energy systems with different renewable sources," International Journal of Hydrogen Energy, vol. 29, pp. 1571-1586, 2004.
M. Alam, S. K. Saha, M. Chowdhury, M. Saifuzzaman, and M. Rahman, "Simulation of solar radiation system," American Journal of Applied Sciences, vol. 2, pp. 751-758, 2005.
L. Olatomiwa, S. Mekhilef, S. Shamshirband, and D. Petković, "Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria," Renewable and Sustainable Energy Reviews, vol. 51, pp. 1784-1791, 2015.
A. Angstrom, "Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation," Quarterly Journal of the Royal Meteorological Society, vol. 50, pp. 121-126, 1924.
B. Yaniktepe and Y. A. Genc, "Establishing new model for predicting the global solar radiation on horizontal surface," International Journal of Hydrogen Energy, vol. 40, pp. 15278-15283, 2015.
H. Khorasanizadeh, K. Mohammadi, and N. Goudarzi, "Prediction of horizontal diffuse solar radiation using clearness index based empirical models; A case study," International Journal of Hydrogen Energy, vol. 41, pp. 21888-21898, 2016.
J. Zhang, L. Zhao, S. Deng, W. Xu, and Y. Zhang, "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, vol. 70, pp. 314-329, 2017.
O. Åženkal and T. Kuleli, "Estimation of solar radiation over Turkey using artificial neural network and satellite data," Applied Energy, vol. 86, pp. 1222-1228, 2009.
J. Qin, Z. Chen, K. Yang, S. Liang, and W. Tang, "Estimation of monthly-mean daily global solar radiation based on MODIS and TRMM products," Applied energy, vol. 88, pp. 2480-2489, 2011.
M. Benghanem, A. Mellit, and S. Alamri, "ANN-based modelling and estimation of daily global solar radiation data: A case study," Energy conversion and management, vol. 50, pp. 1644-1655, 2009.
S. Salisu, M. Mustafa, and M. Mustapha, "Predicting Global Solar Radiation in Nigeria Using Adaptive Neuro-Fuzzy Approach," in International Conference of Reliable Information and Communication Technology, 2017, pp. 513-521.
G. Landeras, J. J. López, O. Kisi, and J. Shiri, "Comparison of Gene Expression Programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain)," Energy conversion and management, vol. 62, pp. 1-13, 2012.
J. Piri, S. Shamshirband, D. Petković, C. W. Tong, and M. H. ur Rehman, "Prediction of the solar radiation on the Earth using support vector regression technique," Infrared Physics & Technology, vol. 68, pp. 179-185, 2015.
S. Bhardwaj, V. Sharma, S. Srivastava, O. Sastry, B. Bandyopadhyay, S. Chandel, et al., "Estimation of solar radiation using a combination of Hidden Markov Model and generalized Fuzzy model," Solar Energy, vol. 93, pp. 43-54, 2013.
J. Wu, C. K. Chan, Y. Zhang, B. Y. Xiong, and Q. H. Zhang, "Prediction of solar radiation with genetic approach combing multi-model framework," Renewable Energy, vol. 66, pp. 132-139, 2014.
S. Shamshirband, K. Mohammadi, L. Yee, D. Petković, and A. Mostafaeipour, "A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation," Renewable and Sustainable Energy Reviews, vol. 52, pp. 1031-1042, 2015.
K. Mohammadi, S. Shamshirband, C. W. Tong, M. Arif, D. Petković, and S. Ch, "A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation," Energy Conversion and Management, vol. 92, pp. 162-171, 2015.
Y. Feng, N. Cui, Q. Zhang, L. Zhao, and D. Gong, "Comparison of artificial intelligence and empirical models for estimation of daily diffuse solar radiation in North China Plain," International Journal of Hydrogen Energy, vol. 42, pp. 14418-14428, 2017.
L. Olatomiwa, S. Mekhilef, S. Shamshirband, K. Mohammadi, D. Petković, and C. Sudheer, "A support vector machine–firefly algorithm-based model for global solar radiation prediction," Solar Energy, vol. 115, pp. 632-644, 2015.
O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, "Choosing multiple parameters for support vector machines," Machine learning, vol. 46, pp. 131-159, 2002.
J. Huang, M. Korolkiewicz, M. Agrawal, and J. Boland, "Forecasting solar radiation on an hourly time scale using a Coupled AutoRegressive and Dynamical System (CARDS) model," Solar Energy, vol. 87, pp. 136-149, 2013.
X. Xue, "Prediction of daily diffuse solar radiation using artificial neural networks," International Journal of Hydrogen Energy, vol. 42, pp. 28214-28221, 2017.
H. M. I. Pousinho, V. M. F. Mendes, and J. P. d. S. Catalão, "A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal," Energy Conversion and Management, vol. 52, pp. 397-402, 2011.
H. M. I. Pousinho, V. M. F. Mendes, and J. P. d. S. Catalão, "Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach," International Journal of Electrical Power & Energy Systems, vol. 39, pp. 29-35, 2012.
X. Yuan, L. Wang, and Y. Yuan, "Application of enhanced PSO approach to optimal scheduling of hydro system," Energy Conversion and Management, vol. 49, pp. 2966-2972, 2008.
J. Catalao, H. Pousinho, and V. Mendes, "Hybrid wavelet-PSO-ANFIS approach for short-term wind power forecasting in Portugal," IEEE Transactions on Sustainable Energy, vol. 2, pp. 50-59, 2011.
J. P. d. S. Catalão, H. M. I. Pousinho, and V. M. F. Mendes, "Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting," IEEE Transactions on Power Systems, vol. 26, pp. 137-144, 2011.
H. Basser, H. Karami, S. Shamshirband, S. Akib, M. Amirmojahedi, R. Ahmad, et al., "Hybrid ANFIS–PSO approach for predicting optimum parameters of a protective spur dike," Applied Soft Computing, vol. 30, pp. 642-649, 2015.
Y. Al-Dunainawi, M. F. Abbod, and A. Jizany, "A new MIMO ANFIS-PSO based NARMA-L2 controller for nonlinear dynamic systems," Engineering Applications of Artificial Intelligence, vol. 62, pp. 265-275, 2017.
M. Rezakazemi, A. Dashti, M. Asghari, and S. Shirazian, "H 2-selective mixed matrix membranes modeling using ANFIS, PSO-ANFIS, GA-ANFIS," International Journal of Hydrogen Energy, 2017.
M. Anemangely, A. Ramezanzadeh, and B. Tokhmechi, "Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: A case study from Ab-Teymour Oilfield," Journal of Natural Gas Science and Engineering, vol. 38, pp. 373-387, 2017.
T. Çavdar, "PSO tuned ANFIS equalizer based on fuzzy C-means clustering algorithm," AEU-International Journal of Electronics and Communications, vol. 70, pp. 799-807, 2016.
R. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on, 1995, pp. 39-43.
X. Li and A. P. Engelbrecht, "Particle swarm optimization: an introduction and its recent developments," in Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, 2007, pp. 3391-3414.
S. Raj, K. C. Ray, and O. Shankar, "Cardiac arrhythmia beat classification using DOST and PSO tuned SVM," Computer methods and programs in biomedicine, vol. 136, pp. 163-177, 2016.
W. Yu and X. Li, "Fuzzy identification using fuzzy neural networks with stable learning algorithms," IEEE Transactions on Fuzzy Systems, vol. 12, pp. 411-420, 2004.
P. P. Bonissone, "Soft computing: the convergence of emerging reasoning technologies," Soft computing, vol. 1, pp. 6-18, 1997.
M. Gil, E. Sarabia, J. Llata, and J. Oria, "Fuzzy c-means clustering for noise reduction, enhancement and reconstruction of 3D ultrasonic images," in Emerging Technologies and Factory Automation, 1999. Proceedings. ETFA'99. 1999 7th IEEE International Conference on, 1999, pp. 465-472.
K. Mohammadi, S. Shamshirband, A. S. Danesh, M. S. Abdullah, and M. Zamani, "Temperature-based estimation of global solar radiation using soft computing methodologies," Theoretical and Applied Climatology, pp. 1-12, 2015.
J.-S. Jang, "ANFIS: adaptive-network-based fuzzy inference system," IEEE transactions on systems, man, and cybernetics, vol. 23, pp. 665-685, 1993.
M. Mustapha, M. Mustafa, S. Khalid, I. Abubakar, and A. M. Abdilahi, "Correlation and Wavelet-based Short-Term Load Forecasting using Anfis," Indian Journal of Science and Technology, vol. 9, 2016.
F. KocabaÅŸ and Åž. Ãœlker, "Estimation of critical submergence for an intake in a stratified fluid media by neuro-fuzzy approach," Environmental Fluid Mechanics, vol. 6, pp. 489-500, 2006.
J.-S. R. Jang, C.-T. Sun, and E. Mizutani, "Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence," 1997.
T. Nguyen and Y. Liao, "Short-Term Load Forecasting Based on Adaptive Neuro-Fuzzy Inference System," Journal of computers, vol. 6, pp. 2267-2271, 2011.
M. Sugeno and G. Kang, "Structure identification of fuzzy model," Fuzzy sets and systems, vol. 28, pp. 15-33, 1988.
S. Hussain and A. AlAlili, "A hybrid solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks," Applied Energy, 2017.
Z. Ramedani, M. Omid, A. Keyhani, S. Shamshirband, and B. Khoshnevisan, "Potential of radial basis function based support vector regression for global solar radiation prediction," Renewable and Sustainable Energy Reviews, vol. 39, pp. 1005-1011, 2014.
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