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Comparison and Prediction of Monthly Average Solar Radiation Data Using Soft Computing Approach for Eastern India

  • Sthitapragyan Mohanty
  • Prashanta Kumar Patra
  • Sudhansu S. Sahoo
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)

Abstract

The paper presents a comparative study of monthly average solar radiation data using different soft computing approaches for Eastern regions of India. Soft computing tools like Multi layer Perceptron (MLP), Artificial Neuro Fuzzy Inference System (ANFIS), and Radial Basis Function (RBF) are used for the said analysis for three cities, i.e. Kolkata, Bhubaneswar and Vishakhapatnam. The input parameters used in these tools are ratio of sunshine hours (S/S0), average temperature ratio (T/T0), relative humidity ratio (R/R0). The monthly average data recorded during the period 1984–1999 for the region mentioned has been considered as training data. The purpose of the study is to compare the accuracy of measured radiation value with the output parameter of solar radiation through different models and using different techniques. The performance of the model is evaluated by comparing the predicted and measured parameters in terms of absolute relative error among ANFIS, MLP and RBF.

Keywords

Solar radiation MLP ANFIS RBF 

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Copyright information

© Springer India 2015

Authors and Affiliations

  • Sthitapragyan Mohanty
    • 1
  • Prashanta Kumar Patra
    • 1
  • Sudhansu S. Sahoo
    • 2
  1. 1.Department of Computer Science & EngineeringC.E.TBhubaneswarIndia
  2. 2.Department of Mechanical EngineeringC.E.TBhubaneswarIndia

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