Abstract

In this work two different machine learning approaches have been studied to predict wind power for different time horizons: individual and global models. The individual approach constructs a model for each horizon while the global approach obtains a single model that can be used for all horizons. Both approaches have advantages and disadvantages. Each individual model is trained with data pertaining to a single horizon, thus it can be specific for that horizon, but can use fewer data for training than the global model, which is constructed with data belonging to all horizons. Support Vector Machines have been used for constructing the individual and global models. This study has been tested on energy production data obtained from the Sotavento wind farm and meteorological data from the European Centre for Medium-Range Weather Forecasts, for a 5 × 5 grid around Sotavento. Also, given the large amount of variables involved, a feature selection algorithm (Sequential Forward Selection) has been used in order to improve the performance of the models. Experimental results show that the global model is more accurate than the individual ones, specially when feature selection is used.

Highlights

  • The correct forecast of the energy obtained from the wind is still one of the main challenges of renewable energies

  • Machine learning methods using Support Vector Machine models (SVM) are proposed to predict wind power at different time horizons, from 3 h until 15 h, using meteorological variables in a grid centered at the Sotavento wind farm

  • Individual models are specific for each step

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Summary

Introduction

The correct forecast of the energy obtained from the wind is still one of the main challenges of renewable energies. They use information from NWP variables and/or historical data to predict power wind, trying to find the relationship between these variables Some of these artificial intelligence techniques are Fuzzy Logic [8], Genetic Algorithms [9], Artificial Neural Networks (ANN) [10], Support Vector Machines (SVM) [11] or ensemble methods [12]. Machine learning methods using Support Vector Machine models (SVM) are proposed to predict wind power at different time horizons (or steps), from 3 h until 15 h, using meteorological variables (from a NWP model) in a grid centered at the Sotavento wind farm. With this purpose two approaches has been studied: individual models and global models.

Individual and Global Approaches for Energy Forecasting
Experimental Results
Conclusions

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