Solar Radiation Forecasting Using Artificial Neural Network

被引:0
作者
Jensona, J. Ida [1 ]
Praynlin, E. [1 ]
机构
[1] VV Coll Engn, Elect & Commun Engn, Tisaiyanvilai, Tamil Nadu, India
来源
2017 INNOVATIONS IN POWER AND ADVANCED COMPUTING TECHNOLOGIES (I-PACT) | 2017年
关键词
Solar radiation forecasting; Back propagation network; Radial Basis Function network; PREDICTION;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Solar radiation is the basic energy source on planet earth. In order to estimate and forecast the solar radiation, it is vital to trace sun's orbit, climatic conditions and dissipation of rays. The function of solar photovoltaic systems is to convert solar energy into electric power. The output power relies on approaching radiation and few features of the intended solar panel. Currently, photovoltaic power is generated in larger amounts. It is necessary that the forecasted data could be efficiently used for controlling and running electricity gauze and to merchandise solar power. In this proposed method, ANNs are used to formulate the solar radiation prediction models. Two different datasets are gathered. The normalization, training and testing processes are done on the gathered historical data. The method used here is supervised learning. The implementation is done by Back Propagation algorithm and Radial basis function network and the results are compared. The prediction accuracy of this method has been studied by various error definitions. The result obtained has greater coincidence between the calculated and the estimated values.
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页数:7
相关论文
共 16 条
[1]   Artificial neural network based daily local forecasting for global solar radiation [J].
Amrouche, Badia ;
Le Pivert, Xavier .
APPLIED ENERGY, 2014, 130 :333-341
[2]  
Angela K., 2011, ADV ARTIF NEURAL SYS, V2011, P1, DOI DOI 10.1155/2011/751908
[3]   Uniform stable radial basis function neural network for the prediction in two mechatronic processes [J].
de Jesus Rubio, Jose ;
Elias, Israel ;
Ricardo Cruz, David ;
Pacheco, Jaime .
NEUROCOMPUTING, 2017, 227 :122-130
[4]  
Devi C.J., 2012, Int. J. Eng. Trends Technol., V3, P19
[5]   Solar radiation estimation using artificial neural networks [J].
Dorvlo, ASS ;
Jervase, JA ;
Al-Lawati, A .
APPLIED ENERGY, 2002, 71 (04) :307-319
[6]  
Hocaoglu F. O., 2016, RENEW ENERG, P1
[7]   Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey [J].
Koca, Ahmet ;
Oztop, Hakan F. ;
Varol, Yasin ;
Koca, Gonca Ozmen .
EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (07) :8756-8762
[8]   Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power [J].
Leva, S. ;
Dolara, A. ;
Grimaccia, F. ;
Mussetta, M. ;
Ogliari, E. .
MATHEMATICS AND COMPUTERS IN SIMULATION, 2017, 131 :88-100
[9]  
Pandey C. K., 2013, J ENERGY, V10
[10]  
Premalatha Neelamegam, 2016, J. appl. res. technol, V14, P206