Publication: A Study of Machine Learning Techniques for Daily Solar Energy Forecasting using Numerical Weather Models
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Identifiers
ISSN: 1860-949X (print)
ISSN: 1860-9503 (online)
ISBN: 978-3-319-10421-8 (print)
ISBN: 978-3-319-10422-5 (online)
UXXI: CC/0000021385
Publication date
2015
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Springer International Publishing
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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.
Description
Proceedings of: 8th International Symposium on Intelligent Distributed Computing (IDC'2014). Madrid, September 3-5, 2014
Keywords
Weather Model, Solar energy forecasting, Information Systems Applications, Artificial Intelligence, Computational Intelligence
Bibliographic citation
Camacho, D. et al. (eds.) (2015) Intelligent Distributed Computing VIII. (Studies in Computational Intelligence, 570). Springer, 269-278.