zbMATH — the first resource for mathematics

ANN-based GA for generating the sizing curve of stand-alone photovoltaic systems. (English) Zbl 1189.78045
Summary: Recent advances in artificial intelligence techniques have allowed the application of such technologies in real engineering problems. In this paper, an artificial neural network-based genetic algorithm (ANN-GA) model was developed for generating the sizing curve of stand-alone photovoltaic (SAPV) systems. Due to the high computing time needed for generating the sizing curves and complex architecture of the neural networks, the genetic algorithm is used in order to find the optimal architecture of the ANN (number of hidden layers and the number of neurons within each hidden layer). Firstly, a numerical method is used for generating the sizing curves for different loss of load probability \((LLP)\) corresponding to 40 sites located in Algeria. The inputs of ANN-GA are the geographical coordinates and the \(LLP\) while the output is the sizing curve represented by \(C_{A} = f(C_{S})\) (i.e., 30-points were taken from each sizing curve). Subsequently, the proposed ANN-GA model has been trained by using a set of 36 sites, whereas data for 4 sites (randomly selected) which are not included in the training dataset have been used for testing the ANN-GA model. The results obtained are compared and tested with those of the numerical method in order to show the effectiveness of the proposed approach.

78A55 Technical applications of optics and electromagnetic theory
68T05 Learning and adaptive systems in artificial intelligence
Full Text: DOI
[1] Mellit, A.; Kalogirou, S. A.: Artificial intelligence techniques for photovoltaic application: a review, Prog energy combust sci 34, 574-632 (2008)
[2] Markvart, T.; Castaner, L.: Practical handbook of photovoltaics: fundamentals and applications, (2003)
[3] Lorenzo, E.: Solar electricity, engineering of photovoltaic systems, (1994)
[4] Aguiar, R.; Collares-Pereira, M.: TAG: a time-dependent, autoregressive, Gaussian model for generating synthetic hourly radiation, Sol energy 49, No. 3, 167-174 (1992)
[5] Aguiar, R. J.; Collares-Perrira, M.; Conde, J. P.: Simple procedure for generating sequences of daily radiation values using library of Markov transition matrices, Sol energy 40, No. 3, 269-279 (1988)
[6] Chapman R. The synthesis solar radiation data for sizing stand-alone PV system. In: 21th IEEE photovoltaic specialists conference; 1990. pp. 965 – 70.
[7] Kumar, R.; Umanand, L.: Estimation of global radiation using clearness index model for sizing photovoltaic system, Renew energy 30, No. 15, 2221-2233 (2005)
[8] Mellit, A.: Artificial intelligence techniques for modeling and forecasting of solar radiation data: a review, Int J artif intell soft comput 1, No. 1, 52-76 (2008)
[9] Koehn P. Combining genetic algorithms and neural networks: the encoding problem. Master dissertation, Dept. Elect, Univ. Tennessee, Knoxville; 1994.
[10] Egido, M.; Lorenzo, E.: The sizing of sand-alone PV systems: a review and a proposed new method, Sol energy mater sol cells 26, No. 1 – 2, 51-69 (1992)
[11] Fragaki, A.; Markvart, T.: Stand-alone PV system design: results using a new sizing approach, Renew energy 33, 162-167 (2008)
[12] Markvart, T.; Fragaki, A.; Ross, J. N.: PV system sizing using observed time series of solar radiation, Sol energy 80, No. 1, 46-50 (2006)
[13] Hontoria, L.; Aguilera, J.; Zufiria, P.: A new approach for sizing stand-alone photovoltaic systems based in neural networks, Sol energy 78, No. 2, 313-319 (2005)
[14] Mellit, A.; Kalogirou, S. A.; Hontoria, L.; Shaari, S.: Artificial intelligence technique for sizing of photovoltaic systems: a review, Renew sustain energy rev 13, 406-419 (2009)
[15] Mellit, A.; Benghanem, M.; Arab, A. Hadj; Guessoum, A.: An adaptive artificial neural network for sizing of stand-alone PV system: application for isolated sites in algeria, Renew energy 30, No. 10, 1501-1524 (2005)
[16] Yao, X.; Liu, Y.: A new evolutionary system for evolving artificial neural networks, IEEE trans neural netw 8, No. 3, 694-713 (1997)
[17] Srinivas, V.; Ramanjaneyulu, K.: An integrated approach for optimum design of Bridge decks using genetic algorithms and artificial neural networks, Adv eng softw 38, 475-487 (2007)
[18] Roberto CP. Evolutionary learning methods for multilayer morphological perceptron. Master dissertation, Dept. Computer Engineering, Univ. Puerto Rico MayagÃ{\(\tfrac14\)}ez Campus; 2004.
[19] www.onm.dz.
[20] Benardos, P. G.; Vosniakos, G. C.: Optimizing feed-forward artificial neural network architecture, Eng appl artif intell 20, No. 2, 365-382 (2007)
[21] Jr., F. J. Massey: The Kolmogorov-Smirnov test for goodness of fit, J am stat assoc 46, 68-78 (1951) · Zbl 0042.14403
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.