Very short term forecasting in global solar irradiance using linear and nonlinear models

The behavior of the Photovoltaic Systems are based on solar radiation that focuses on the panels, thus the generation of energy depends on this resource. Planning of these systems makes that demanding energy can be interrupted due to variant radiation behavior. Very short term forecasting models can be useful to improve the available supplies. Present proposal allows a first approach for term shorter prediction of global solar irradiance. Linear and nonlinear models were compared to implement this forecasting. Results show that nonlinear models based on computational intelligence techniques provide better results with a simpler methodology to get the models.

[1]  R. Inman,et al.  Solar forecasting methods for renewable energy integration , 2013 .

[2]  Andreas Holzinger,et al.  Data Mining with Decision Trees: Theory and Applications , 2015, Online Inf. Rev..

[3]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[4]  Bangyin Liu,et al.  Online 24-h solar power forecasting based on weather type classification using artificial neural network , 2011 .

[5]  Nadejda Komendantova,et al.  Renewables 2012 Global Status Report , 2012 .

[6]  Pierluigi Siano,et al.  Demand response and smart grids—A survey , 2014 .

[7]  H. Mori,et al.  A data mining method for selecting input variables for forecasting model of global solar radiation , 2012, PES T&D 2012.

[8]  Jingfei Yang,et al.  Short-term load forecasting with increment regression tree , 2006 .

[9]  Stefan Fritsch,et al.  neuralnet: Training of Neural Networks , 2010, R J..

[10]  Dobrivoje Popovic,et al.  Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications (Advances in Industrial Control) , 2005 .

[11]  M. Diagne,et al.  Review of solar irradiance forecasting methods and a proposition for small-scale insular grids , 2013 .

[12]  Akihiko Yokoyama,et al.  Smart Grid: Technology and Applications , 2012 .

[13]  Eduardo F. Fernández,et al.  A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator , 2014 .

[14]  Kelum A. A. Gamage,et al.  Demand side management in smart grid: A review and proposals for future direction , 2014 .

[15]  H. Pedro,et al.  Assessment of forecasting techniques for solar power production with no exogenous inputs , 2012 .

[16]  E. Larsen,et al.  How capacity mechanisms drive technology choice in power generation: The case of Colombia , 2016 .

[17]  John Boland,et al.  Forecasting solar radiation on an hourly time scale using a Coupled AutoRegressive and Dynamical System (CARDS) model , 2013 .

[18]  Mehdi Khashei,et al.  An artificial neural network (p, d, q) model for timeseries forecasting , 2010, Expert Syst. Appl..