Affiliation:
1. Universidad Militar Nueva Granada
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.
Publisher
Trans Tech Publications, Ltd.
Reference16 articles.
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).
5. K. Trangbaek, M. Pedersen, J. Bendtsen and J. Stoustrup, Predictive smart grid control with exact aggregated power constraints, Springer, 2012, pp.649-668.