Comparison and Sensitivity Analysis of Methods for Solar PV Power Prediction

  • Mashud RanaEmail author
  • Ashfaqur Rahman
  • Liwan Liyanage
  • Mohammed Nazim Uddin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11154)


The variable nature of solar power output from PhotoVoltaic (PV) systems is the main obstacle for penetration of such power into the electricity grid. Thus, numerous methods have been proposed in the literature to construct forecasting models. In this paper, we present a comprehensive comparison of a set of prominent methods that utilize weather prediction for future. Firstly, we evaluate the prediction accuracy of widely used Neural Network (NN), Support Vector Regression (SVR), k-Nearest Neighbours (kNN), Multiple Linear Regression (MLR), and two persistent methods using four data sets for 2 years. We then analyze the sensitivities of their prediction accuracy to 10–25% possible error in the future weather prediction obtained from the Bureau of Meteorology (BoM). Results demonstrate that ensemble of NNs is the most promising method and achieves substantial improvement in accuracy over other prediction methods.


Solar power prediction Sensitivity analysis Neural networks Support Vector Regression Nearest neighbours Regression 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mashud Rana
    • 1
    Email author
  • Ashfaqur Rahman
    • 2
  • Liwan Liyanage
    • 3
  • Mohammed Nazim Uddin
    • 4
  1. 1.Data61, CSIROSydneyAustralia
  2. 2.Data61, CSIROSandy BayAustralia
  3. 3.School of Computing, Engineering and MathematicsWestern Sydney UniversitySydneyAustralia
  4. 4.Department of Computer Science and EngineeringEast Delta UniversityChittagongBangladesh

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