Evaluation of electrical efficiency of photovoltaic thermal solar collector

References

• Ahmad, S. H. A., Saidur, R., Mahbubul, I. M., & Al-Sulaiman, F. A. (2017). Optical properties of various nanofluids used in solar collector: A review. Renewable and Sustainable Energy Reviews, 73, 1014–1030. doi: 10.1016/j.rser.2017.01.173
   Web of Science ®Google Scholar
• Ahmadi, M. H., Ghazvini, M., Sadeghzadeh, M., Alhuyi Nazari, M., Kumar, R., Naeimi, A., & Ming, T. (2018). Solar power technology for electricity generation: A critical review. Energy Science and Engineering. doi: 10.1002/ese3.239
   Google Scholar
• Al-Maamary, H. M. S., Kazem, H. A., & Chaichan, M. T. (2017). Renewable energy and GCC states energy challenges in the 21st century: A review. International Journal of Computation and Applied Sciences, 2(1), 11–18. doi: 10.24842/1611/0018
   Google Scholar
• Andrews, D. F. (1972). Plots of high-dimensional data. Biometrics, 28(1), 125–136. doi: 10.2307/2528964
   Web of Science ®Google Scholar
• Baghban, A., Bahadori, M., Lemraski, A. S., & Bahadori, A. (2015). Prediction of solubility of ammonia in liquid electrolytes using least square support vector machines. Ain Shams Engineering Journal. doi: 10.1016/j.asej.2016.08.006
   Web of Science ®Google Scholar
• Baghban, A., Kashiwao, T., Bahadori, M., Ahmad, Z., & Bahadori, A. (2016). Estimation of natural gases water content using adaptive neuro-fuzzy inference system. Petroleum Science and Technology, 34(10), doi: 10.1080/10916466.2016.1176039
   Web of Science ®Google Scholar
• Baghban, A., Sasanipour, J., & Zhang, Z. (2018). A new chemical structure-based model to estimate solid compound solubility in supercritical CO2. Journal of CO2 Utilization, 26, 262–270. doi: 10.1016/j.jcou.2018.05.009
   Web of Science ®Google Scholar
• Bahadori, A., Baghban, A., Bahadori, M., Lee, M., Ahmad, Z., Zare, M., & Abdollahi, E. (2016). Computational intelligent strategies to predict energy conservation benefits in excess air controlled gas-fired systems. Applied Thermal Engineering, 102, 432–446. doi: 10.1016/j.applthermaleng.2016.04.005
   Web of Science ®Google Scholar
• Bong, C. P. C., Ho, W. S., Hashim, H., Lim, J. S., Ho, C. S., Tan, W. S. P., & Lee, C. T. (2017). Review on the renewable energy and solid waste management policies towards biogas development in Malaysia. Renewable and Sustainable Energy Reviews, 70, 988–998. doi: 10.1016/j.rser.2016.12.004
   Web of Science ®Google Scholar
• Brown, M., & Harris, C. J. (1994). Neurofuzzy adaptive modelling and control. Texas, USA: Prentice Hall.
   Google Scholar
• Burrows, W. R. (1997). CART regression models for predicting UV radiation at the ground in the presence of cloud and other environmental factors. Journal of Applied Meteorology, 36(5), 531–544. doi: 10.1175/1520-0450(1997)036<0531:CRMFPU>2.0.CO;2
   Google Scholar
• Caner, M., Gedik, E., & Keçebaş, A. (2011). Investigation on thermal performance calculation of two type solar air collectors using artificial neural network. Expert Systems with Applications, 38(3), 1668–1674. doi: 10.1016/j.eswa.2010.07.090
   Web of Science ®Google Scholar
• Chau, K. (2017). Use of meta-heuristic techniques in rainfall-runoff modelling. Multidisciplinary Digital Publishing Institute.
   Google Scholar
• Chuntian, C., & Chau, K.-W. (2002). Three-person multi-objective conflict decision in reservoir flood control. European Journal of Operational Research, 142(3), 625–631. doi: 10.1016/S0377-2217(01)00319-8
   Web of Science ®Google Scholar
• Dubey, S., Sandhu, G. S., & Tiwari, G. N. (2009). Analytical expression for electrical efficiency of PV/T hybrid air collector. Applied Energy, 86(5), 697–705. doi: 10.1016/j.apenergy.2008.09.003
   Web of Science ®Google Scholar
• Fotovatikhah, F., Herrera, M., Shamshirband, S., Chau, K.-W., Faizollahzadeh Ardabili, S., & Piran, M. J. (2018). Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work. Engineering Applications of Computational Fluid Mechanics, 12(1), 411–437. doi: 10.1080/19942060.2018.1448896
   Web of Science ®Google Scholar
• Fuqiang, W., Ziming, C., Jianyu, T., Yuan, Y., Yong, S., & Linhua, L. (2017). Progress in concentrated solar power technology with parabolic trough collector system: A comprehensive review. Renewable and Sustainable Energy Reviews, 79, 1314–1328. doi: 10.1016/j.rser.2017.05.174
   Web of Science ®Google Scholar
• Hajikhodaverdikhan, P., Nazari, M., Mohsenizadeh, M., Shamshirband, S., & Chau, K. (2018). Earthquake prediction with meteorological data by particle filter-based support vector regression. Engineering Applications of Computational Fluid Mechanics, 12(1), 679–688. doi: 10.1080/19942060.2018.1512010
   Web of Science ®Google Scholar
• Haratipour, P., Baghban, A., Mohammadi, A. H., Nazhad, S. H. H., & Bahadori, A. (2017). On the estimation of viscosities and densities of CO2-loaded MDEA, MDEA + AMP, MDEA + DIPA, MDEA + MEA, and MDEA + DEA aqueous solutions. Journal of Molecular Liquids, 242, 146–159. doi: 10.1016/j.molliq.2017.06.123
   Web of Science ®Google Scholar
• Haykin, S. (1994). Neural networks: A comprehensive foundation. Seattle, USA: Prentice Hall PTR.
   Google Scholar
• Haykin, S. S., Haykin, S. S., Haykin, S. S., & Haykin, S. S. (2009). Neural networks and learning machines (Vol. 3). Upper Saddle River, NJ, USA: Pearson.
   Google Scholar
• Hussain, F., Othman, M. Y. H., Sopian, K., Yatim, B., Ruslan, H., & Othman, H. (2013). Design development and performance evaluation of photovoltaic/thermal (PV/T) air base solar collector. Renewable and Sustainable Energy Reviews, 25, 431–441. doi: 10.1016/j.rser.2013.04.014
   Web of Science ®Google Scholar
• Kalani, H., Sardarabadi, M., & Passandideh-Fard, M. (2017). Using artificial neural network models and particle swarm optimization for manner prediction of a photovoltaic thermal nanofluid based collector. Applied Thermal Engineering, 113, 1170–1177. doi: 10.1016/j.applthermaleng.2016.11.105
   Web of Science ®Google Scholar
• Kannan, N., & Vakeesan, D. (2016). Solar energy for future world:-A review. Renewable and Sustainable Energy Reviews, 62, 1092–1105. doi: 10.1016/j.rser.2016.05.022
   Web of Science ®Google Scholar
• Kramer, K., & Helmers, H. (2013). The interaction of standards and innovation: Hybrid photovoltaic–thermal collectors. Solar Energy, 98, 434–439. doi: 10.1016/j.solener.2013.08.042
   Web of Science ®Google Scholar
• Kumar, A., Prakash, O., & Kaviti, A. K. (2017). A comprehensive review of Scheffler solar collector. Renewable and Sustainable Energy Reviews, 77, 890–898. doi: 10.1016/j.rser.2017.03.044
   Web of Science ®Google Scholar
• Lewis, N. S. (2016). Research opportunities to advance solar energy utilization. Science, 351(6271), aad1920. doi: 10.1126/science.aad1920
   PubMed Web of Science ®Google Scholar
• Lin, C.-T., & Lee, C. S. G. (1996). Neural fuzzy systems. Texas, USA: PTR Prentice Hall.
   Google Scholar
• Marquez, R., & Coimbra, C. F. M. (2011). Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database. Solar Energy, 85(5), 746–756. doi: 10.1016/j.solener.2011.01.007
   Web of Science ®Google Scholar
• Mitchell, T. M. (1997). Artificial neural networks. Machine Learning, 45, 81–127.
   Google Scholar
• Modi, A., Bühler, F., Andreasen, J. G., & Haglind, F. (2017). A review of solar energy based heat and power generation systems. Renewable and Sustainable Energy Reviews, 67, 1047–1064. doi: 10.1016/j.rser.2016.09.075
   Web of Science ®Google Scholar
• Moreno, A., Gilabert, M. A., & Martínez, B. (2011). Mapping daily global solar irradiation over Spain: A comparative study of selected approaches. Solar Energy, 85(9), 2072–2084. doi: 10.1016/j.solener.2011.05.017
   Web of Science ®Google Scholar
• Pandey, K. M., & Chaurasiya, R. (2017). A review on analysis and development of solar flat plate collector. Renewable and Sustainable Energy Reviews, 67, 641–650. doi: 10.1016/j.rser.2016.09.078
   Web of Science ®Google Scholar
• Pandey, A. K., Tyagi, V. V., Jeyraj, A., Selvaraj, L., Rahim, N. A., & Tyagi, S. K. (2016). Recent advances in solar photovoltaic systems for emerging trends and advanced applications. Renewable and Sustainable Energy Reviews, 53, 859–884. doi: 10.1016/j.rser.2015.09.043
   Web of Science ®Google Scholar
• Park, J., & Sandberg, I. W. (1993). Approximation and radial-basis-function networks. Neural Computation, 5(2), 305–316. doi: 10.1162/neco.1993.5.2.305
   Web of Science ®Google Scholar
• Pelckmans, K., Suykens, J. A. K., Gestel, T. V., De Brabanter, J., Lukas, L., Hamers, B., … Vandewalle, J. (n.d.). LS-SVMlab: a MATLAB/C toolbox for Least Squares Support Vector Machines. Retrieved from ftp://ftp.esat.kuleuven.be/stadius/kpelckma/kp02-44.pdf.
   Google Scholar
• Podestá, G. P., Núñez, L., Villanueva, C. A., & Skansi, M. A. (2004). Estimating daily solar radiation in the Argentine Pampas. Agricultural and Forest Meteorology, 123(1–2), 41–53. doi: 10.1016/j.agrformet.2003.11.002
   Web of Science ®Google Scholar
• Qin, Y. (2015). A review on the development of cool pavements to mitigate urban heat island effect. Renewable and Sustainable Energy Reviews, 52, 445–459. doi: 10.1016/J.RSER.2015.07.177
   Web of Science ®Google Scholar
• Qin, Y. (2016). Pavement surface maximum temperature increases linearly with solar absorption and reciprocal thermal inertial. International Journal of Heat and Mass Transfer, 97, 391–399. doi: 10.1016/J.IJHEATMASSTRANSFER.2016.02.032
   Web of Science ®Google Scholar
• Qin, Y., & Hiller, J. E. (2016). Water availability near the surface dominates the evaporation of pervious concrete. Construction and Building Materials, 111, 77–84. doi: 10.1016/J.CONBUILDMAT.2016.02.063
   Web of Science ®Google Scholar
• Qin, Y., Hiller, J. E., & Bao, T. (2013). Modeling cold region ground temperatures with a heat flux upper boundary model. Journal of Cold Regions Engineering, 27(1), 29–43. doi: 10.1061/(ASCE)CR.1943-5495.0000051
   Web of Science ®Google Scholar
• Qin, Y., Liang, J., Luo, Z., Tan, K., & Zhu, Z. (2016). Increasing the southern side-slope albedo remedies thermal asymmetry of cold-region roadway embankments. Cold Regions Science and Technology, 123, 115–120. doi: 10.1016/J.COLDREGIONS.2015.12.006
   Web of Science ®Google Scholar
• Qin, Y., Liang, J., Tan, K., & Li, F. (2016). A side by side comparison of the cooling effect of building blocks with retro-reflective and diffuse-reflective walls. Solar Energy, 133, 172–179. doi: 10.1016/J.SOLENER.2016.03.067
   Web of Science ®Google Scholar
• Qin, Y., Liang, J., Tan, K., & Li, F. (2017). The amplitude and maximum of daily pavement surface temperature increase linearly with solar absorption. Road Materials and Pavement Design, 18(2), 440–452. doi: 10.1080/14680629.2016.1162732
   Web of Science ®Google Scholar
• Qin, Y., Zhang, M., & Hiller, J. E. (2017). Theoretical and experimental studies on the daily accumulative heat gain from cool roofs. Energy, 129, 138–147. doi: 10.1016/J.ENERGY.2017.04.077
   Web of Science ®Google Scholar
• Ramezanizadeh, M., Ahmadi, M. A., Ahmadi, M. H., & Alhuyi Nazari, M. (2018). Rigorous smart model for predicting dynamic viscosity of Al2O3/water nanofluid. Journal of Thermal Analysis and Calorimetry, 1. doi: 10.1007/s10973-018-7916-1
   Web of Science ®Google Scholar
• Ramezanizadeh, M., Alhuyi Nazari, M., Ahmadi, M. H., Lorenzini, G., & Pop, I. (2019). A review on the applications of intelligence methods in predicting thermal conductivity of nanofluids. Journal of Thermal Analysis and Calorimetry, 1–17. doi: 10.1007/s10973-019-08154-3
   Web of Science ®Google Scholar
• Ramezanizadeh, M., Nazari, M. A., Ahmadi, M. H., Lorenzini, G., Kumar, R., & Jilte, R. (2018). A review on the solar applications of thermosyphons. Mathematical Modelling of Engineering Problems, 5(4), 275–280. doi: 10.18280/mmep.050401
   Google Scholar
• Rejeb, O., Dhaou, H., & Jemni, A. (2015). A numerical investigation of a photovoltaic thermal (PV/T) collector. Renewable Energy, 77, 43–50. doi: 10.1016/j.renene.2014.12.012
   Web of Science ®Google Scholar
• Rousseeuw, P. J., & Leroy, A. M. (2005). Robust regression and outlier detection (Vol. 589). John wiley & sons.
   Google Scholar
• Schalkoff, R. J. (1997). Artificial neural networks (Vol. 1). New York: McGraw-Hill.
   Google Scholar
• Sijm, J. P. M. (2017). Cost and revenue related impacts of integrating electricity from variable renewable energy into the power system-A review of recent literature. Policy Studies, 2016, 2015.
   Google Scholar
• Suykens, J. A. K., Van Gestel, T., De Brabanter, J., De Moor, B., & Vandewalle, J. (2002). Least squares support vector machines. Singapore: World Scientific Publishing.
   Google Scholar
• Taherei Ghazvinei, P., Hassanpour Darvishi, H., Mosavi, A., Yusof, K. bin W., Alizamir, M., Shamshirband, S., & Chau, K. (2018). Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network. Engineering Applications of Computational Fluid Mechanics, 12(1), 738–749. doi: 10.1080/19942060.2018.1526119
   Web of Science ®Google Scholar
• Tso, G. K. F., & Yau, K. K. W. (2007). Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy, 32(9), 1761–1768. doi: 10.1016/j.energy.2006.11.010
   Web of Science ®Google Scholar
• Twidell, J., & Weir, T. (2015). Renewable energy resources. Seattle, USA: Routledge.
   Google Scholar
• Vapnik, V., Golowich, S. E., & Smola, A. J. (1997). Support vector method for function approximation, regression estimation and signal processing. In Advances in neural information processing systems (pp. 281–287).
   Google Scholar
• Varol, Y., Koca, A., Oztop, H. F., & Avci, E. (2010). Forecasting of thermal energy storage performance of Phase Change Material in a solar collector using soft computing techniques. Expert Systems with Applications, 37(4), 2724–2732. doi: 10.1016/j.eswa.2009.08.007
   Web of Science ®Google Scholar
• Wagh, S., & Walke, P. V. (2017). Review on wind-solar hybrid power system. International Journal of Research In Science & Engineering, 3, 112–132.
   Google Scholar
• Wasserman, P. D. (1993). Advanced methods in neural computing. Florida, USA: John Wiley & Sons, Inc.
   Google Scholar
• Wu, C. L., & Chau, K. W. (2011). Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis. Journal of Hydrology, 399(3–4), 394–409. doi: 10.1016/J.JHYDROL.2011.01.017
   Web of Science ®Google Scholar
• Ye, J., & Xiong, T. (2007). Svm versus least squares svm. In International Conference on Artificial Intelligence and Statistics (pp. 644–651).
   Google Scholar
• Yegnanarayana, B. (2009). Artificial neural networks. New York, USA: PHI Learning Pvt. Ltd.
   Google Scholar