Comparative Study of Various Models to Estimate Hourly Solar Irradiance: Application for Performance Analysis of a Renewable Energy DC-Micro Grid

Abstract:

This paper describes the study and analysis of different techniques for online solar irradiance prediction algorithms to properly estimate over the 24 hours of the next day in the “Universidad Militar Nueva Granada” (UMNG) campus at Cajicá, Colombia, in order to use predictions for a model predicted control of a DC-micro grid. These models were designed and tested using MATLAB® software. The performance of models were evaluated and compared among them to determine the best forecasting approach for Cajicá. The absence of seasons and the noisy solar irradiance time series caused by cloudy covering as perturbation are the main particularity of the Cajicá’s climate behavior. A meteorological database from 2010 to 2014 was used to estimate or train the model of prediction ARMAX and NNF, NAR, NARX as Artificial Neural Networks (ANNs), which were compared with error criteria such as square and absolute error criteria.

• Periodical: Applied Mechanics and Materials (Volume 700)
• Pages: 7-11
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.700.7
• Online since: December 2014
• Authors: Mauricio Mauledoux*, Oscar I. Caldas, Edilberto Mejía-Ruda
• Keywords: Artificial Neural Network (ANN), DC Smart-Grid, Prediction Model, Solar Radiation

References

[1] D. Salomonsson, Modeling, control and protection of low-voltage DC microgrids, (2008).

[2] P. Heer, Decentralized model predictive control for smart grid applications, (2013).

[3] J. Maciejowski, Predictive control with constraints, U. o. C. Department of Engineering, Ed., Prentice Hal, (2001).

[4] B. Picasso, D. D. Vito, R. Scattolini and P. Colaneri, An MPC approach to the design of two-layer hierarchical control systems, Automatica, vol. 46, pp.823-831, (2010).

DOI: 10.1016/j.automatica.2010.02.013

[5] K. Trangbaek, M. Pedersen, J. Bendtsen and J. Stoustrup, Predictive smart grid control with exact aggregated power constraints, Springer, 2012, pp.649-668.

DOI: 10.1007/978-3-642-21578-0_21

[6] S. Rehman, Empirical model development and comparison with existing correlations, Applied Energy, vol. 64, pp.369-378, (1999).

DOI: 10.1016/s0306-2619(99)00108-7

[7] M. Paulescua, L. Farab and E. Tulcan-Paulescu, Models for obtaining daily global solar irradiation from air temperature data, Atmosphere Research, vol. 79, pp.277-240, (2006).

DOI: 10.1016/j.atmosres.2005.06.001

[8] A. Mellita, S. Kalogiroub, S. Shaaric, H. Salhid and A. H. Arab, Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system, Renewable Energy, vol. 33, pp.1570-1590, (2008).

DOI: 10.1016/j.renene.2007.08.006

[9] L. Hontoria, J. Aguilera and P. Zufiria, Generation of hourly irradiation synthetic series using the neural network multilayer perceptron, Solar Energy, vol. 72, pp.441-446, (2002).

DOI: 10.1016/s0038-092x(02)00010-5

[10] A. Mellit, H. Eleuch, Benghanem, C. Elaoun and A. M. Pavan, An adaptive model for predicting of global, direct and diffuse hourly solar irradiance, Energy Conversion and Management, vol. 51, pp.771-782, (2010).

DOI: 10.1016/j.enconman.2009.10.034

[11] L. Hontoria, J. Aguilera and P. Zufiria, Generation of hourly irradiation synthetic series using the neural network multilayer perceptron, Solar Energy, vol. 72, pp.441-446, (2002).

DOI: 10.1016/s0038-092x(02)00010-5

[12] A. Mellita and S. A. Kalogirou, Artificial intelligence techniques for photovoltaic applications: A review, Progress in Energy and Combustion Science, vol. 34, pp.574-632, (2008).

DOI: 10.1016/j.pecs.2008.01.001

[13] E. y. B. C. Camacho, Model predictive control, London: Springer-Verlag, (2004).

[14] S. Kocúr, Identification and modelling of linear dynamic systems, Advances in Electrical and Electronic Engineering, vol. 5, pp.140-142, (2011).

[15] S. A. Zulkeflee, S. A. Sata and N. Aziz, Advanced model predictive control, D. T. ZHENG, Ed., InTech, (2011).

[16] A Comparison between GAs and PSO in training ANN to model the TE chemical process reactor, http: /www. aisb. org. uk/convention/aisb08/, (2008).