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A Study of Machine Learning Techniques for Daily Solar Energy Forecasting Using Numerical Weather Models

  • Ricardo AlerEmail author
  • Ricardo Martín
  • José M. Valls
  • Inés M. Galván
Part of the Studies in Computational Intelligence book series (SCI, volume 570)

Abstract

Forecasting solar energy is becoming an important issue in the context of renewable energy sources and Machine Learning Algorithms play an important rule in this field. The prediction of solar energy can be addressed as a time series prediction problem using historical data. Also, solar energy forecasting can be derived from numerical weather prediction models (NWP). Our interest is focused on the latter approach.We focus on the problem of predicting solar energy from NWP computed from GEFS, the Global Ensemble Forecast System, which predicts meteorological variables for points in a grid. In this context, it can be useful to know how prediction accuracy improves depending on the number of grid nodes used as input for the machine learning techniques. However, using the variables from a large number of grid nodes can result in many attributes which might degrade the generalization performance of the learning algorithms. In this paper both issues are studied using data supplied by Kaggle for the State of Oklahoma comparing Support Vector Machines and Gradient Boosted Regression. Also, three different feature selection methods have been tested: Linear Correlation, the ReliefF algorithm and, a new method based on local information analysis.

Keywords

Support Vector Machine Ensemble Member Grid Node Machine Learning Technique Feature Selection Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ricardo Aler
    • 1
    Email author
  • Ricardo Martín
    • 1
  • José M. Valls
    • 1
  • Inés M. Galván
    • 1
  1. 1.Computer Science DepartmentCarlos III UniversityGetafeSpain

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