Abstract
Since the BIPVS offers the possibility to replace part of the traditional building materials, with a possible price reduction, in comparison to a classic rooftop installation [1, 2], then the correct estimation of system level performances, system reliability and system availability is becoming more important and popular among installers, integrators, investors and owners; with that purpose several tools and models were developed [3–5]. The combination of different phenomena, such as solar radiation available on site, dust presence, shadowing or UV radiation over long outdoor exposure, affect in different ways the real performance of BIPV systems and thus the related economic evaluations [6–8].
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
A. Campoccia, L. Dusonchet, E. Telaretti, G. Zizzo, An analysis of feed’ in tariffs for solar PV in six representative countries of the European Union. Sol. Energy 107, 530–542 (2014.) https://doi.org/10.1016/j.solener.2014.05.047
T. James, A. Goodrich, M. Woodhouse, R. Margolis, S. Ong, Building-integrated photovoltaics (BIPV) in the residential sector: an analysis of installed rooftop system prices. Energy 50 (2011). https://doi.org/10.2172/1029857
V.J. Chin, Z. Salam, K. Ishaque, Cell modelling and model parameters estimation techniques for photovoltaic simulator application: a review. Appl. Energy 154, 500–519 (2015). https://doi.org/10.1016/j.apenergy.2015.05.035
V. Lo Brano, G. Ciulla, M.D. Falco, Artificial neural networks to predict the power output of a PV panel. Int. J. Photoenergy 2014, 193083 (2014)
W. Zhou, H. Yang, Z. Fang, A novel model for photovoltaic array performance prediction. Appl. Energy 84, 1187–1198 (2007). https://doi.org/10.1016/j.apenergy.2007.04.006
V. Sharma, S.S. Chandel, Performance and degradation analysis for long term reliability of solar photovoltaic systems: A review. Renew. Sust. Energ. Rev. 27, 753–767 (2013). https://doi.org/10.1016/j.rser.2013.07.046
F.R. Chica, J. Aristizábal, Application of autoregressive model with exogenous inputs to identify and analyse patterns of solar global radiation and ambient temperature. Int J Ambient Energy 33(4), 177–183 (2012)
J. Aristizábal, G. Gordillo, Performance and economic evaluation of the first grid – connected installation in Colombia over 4 years of continuous operation. Int J Sustain Energy 30(1), 34–46 (2011)
T. Ikegami, T. Maezono, F. Nakanishi, Y. Yamagata, K. Ebihara, Estimation of equivalent circuit parameters of PV module and its application to optimal operation of PV system. Sol. Energy Mater. Sol. Cells 67(1–4), 389e395 (2001)
A.N. Celik, N. Acikgoz, Modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules using four- and five-parameter models. Appl. Energy 84(1), 1–15 (2007)
J.D. Mondol, Y.G. Yohanis, B. Norton, Comparison of measured and predicted long term performance of grid a connected photovoltaic system. Energy Convers. Manag. 48(4), 1065–1080 (2007)
K. Ishaque, Z. Salam, H. Taheri, Simple, fast and accurate two-diode model for photovoltaic modules. Sol. Energy Mater. Sol. Cells 95(2), 586–594 (2011)
K. Ishaque, Z. Salam, H. Taheri, Syafaruddin, Modeling and simulation of photovoltaic (PV) system during partial shading based on a two-diode model. Simul. Model. Pract. Theory 19(7), 1613–1626 (2011)
M.G. Villalva, J.R. Gazoli, E. Ruppert, Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans. Power Electr. 24(5–6), 1198–1208 (2009)
Y. Mahmoud, W. Xiao, H.H. Zeineldin, A simple approach to modeling and simulation of photovoltaic modules. IEEE Trans. Sustain. Energy 3(1), 185–186 (2012)
R. Chenni, M. Makhlouf, T. Kerbache, A. Bouzid, A detailed modeling method for photovoltaic cells. Energy 32(9), 1724–1730 (2007)
F. Bonanno, G. Capizzi, G. Graditi, C. Napoli, G.M. Tina, A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module. Appl. Energy 97, 956–961 (2012)
E. Karatepe, M. Boztepe, M. Colak, Neural network based solar cell model. Energy Convers. Manag. 47(9–10), 1159–1178 (2006)
K. Ishaque, Z. Salam, An improved modeling method to determine the model parameters of photovoltaic (PV) modules using differential evolution (DE). Sol. Energy 85(9), 2349–2359 (2011)
M. Zagrouba, A. Sellami, M. Bouaicha, M. Ksouri, Identification of PV solar cells and modules parameters using the genetic algorithms: application to maximum power extraction. Sol. Energy 84(5), 860–866 (2010)
L. Sandrolini, M. Artioli, U. Reggiani, Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis. Appl. Energy 87(2), 442–451 (2010)
C. Huang, A. Bensoussan, M. Edesess, K.L. Tsui, Improvement in artificial neural network-based estimation of grid connected photovoltaic power output. Renew. Energy 97, 838–848 (2016)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Aristizábal Cardona, A.J., Páez Chica, C.A., Ospina Barragán, D.H. (2018). Application of Neural Networks to Validate the Power Generation of BIPVS. In: Building-Integrated Photovoltaic Systems (BIPVS). Springer, Cham. https://doi.org/10.1007/978-3-319-71931-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-71931-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71930-6
Online ISBN: 978-3-319-71931-3
eBook Packages: EnergyEnergy (R0)