Solar radiation prediction using statistical approaches

Statistical approach is often used in time series analysis. One of its uses is to predict the future trend of a time series. This can be applied in many applications such as solar radiation, economics and other researches related to time series. In this paper, we use several classic statistical models to fit the solar radiation time series. The goal is to find a suitable radiation model in predicting the trend of solar radiation time series. The simulation result shows that Linear Regression has better performance than other models such as the Auto Regression, Auto Regression Integrate Moving Average. The Linear Regression method requires a number of previous data for prediction. Simulation shows that a list of 10 to 15 past data values yields optimal result.

[1]  Ahmet Arisoy,et al.  Solar energy potential for heating and cooling systems in big cities of Turkey , 2002 .

[2]  I. Dincer,et al.  A simple technique for estimating solar radiation parameters and its application for Gebze , 1996 .

[3]  Julian J. Faraway,et al.  Practical Regression and Anova using R , 2002 .

[4]  Turgay Tugay Bilgin,et al.  A Data Mining Application on Air Temperature Database , 2004, ADVIS.

[5]  M. C. Acock,et al.  Estimating Missing Weather Data for Agricultural Simulations Using Group Method of Data Handling , 2000 .

[6]  Kamil Kaygusuz,et al.  Renewable energy potential and utilization in Turkey. , 2003 .

[7]  G. J. Janacek,et al.  Practical Time Series , 2001 .

[8]  Saifur Rahman,et al.  Simulation of photovoltaic power systems and their performance prediction , 1988 .

[9]  E. Arcaklioğlu,et al.  Use of artificial neural networks for mapping of solar potential in Turkey , 2004 .

[10]  Simon Parsons,et al.  Principles of Data Mining by David J. Hand, Heikki Mannila and Padhraic Smyth, MIT Press, 546 pp., £34.50, ISBN 0-262-08290-X , 2004, The Knowledge Engineering Review.

[11]  Jeff Dean,et al.  Time Series , 2009, Encyclopedia of Database Systems.

[12]  N. Robinson.  Solar radiation , 2020, Advanced Remote Sensing.

[13]  S. N. Kaplanis,et al.  New methodologies to estimate the hourly global solar radiation; Comparisons with existing models , 2006 .

[14]  C. Pipper,et al.  [''R"--project for statistical computing]. , 2008, Ugeskrift for laeger.

[15]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.