Comparing different solutions for forecasting the energy production of a wind farm
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S.I. : ADVANCES IN BIO-INSPIRED INTELLIGENT SYSTEMS
Comparing different solutions for forecasting the energy production of a wind farm Darı´o Baptista1,2 • Joa˜o Paulo Carvalho2 • Fernando Morgado-Dias1,3 Received: 5 January 2018 / Accepted: 11 July 2018 The Natural Computing Applications Forum 2018
Abstract The production of different renewable and non-renewable energies sources can be coordinated efficiently to avoid costly overproduction. For that, it is important to develop models for accurate energy production forecasting. The energy production of wind farms is extremely dependent on the meteorological conditions. In this paper, computational intelligence techniques were used to predict the production of energy in a wind farm. This study is held on publicly accessible climacteric and energy data for a wind farm in Galicia, Spain, with 24 turbines of 9 different models. Data preprocessing was performed in order to delete outliers caused by the maintenance and technical problems. Models of the following types were developed: artificial neural networks, support vector machines and adaptive neuro-fuzzy inference system models. Furthermore, the persistence method was used as a time series forecast baseline model. Overall, the developed computational intelligence models perform better than the baseline model, being adaptive neuro-fuzzy inference system the model with the best results: a * 5% performance improvement over the baseline model. Keywords Support vector machine Artificial neural network Adaptive neuro-fuzzy inference system Eolic energy
1 Introduction Currently, there are several ways to get energy from different natural resources. Energy production of wind farms is rapidly expanding into a large-scale industry. This kind of resource is non-polluting and has the advantage of being installed in remote places that were previously not covered by the electrical grid. For that reason, wind farms have been increasing in many European countries, and as an example, 4% of the energy consumption in Spain already originates from wind farms [1].
& Darı´o Baptista [email protected] Joa˜o Paulo Carvalho [email protected] Fernando Morgado-Dias [email protected] 1
M-ITI, Madeira Interactive Technologies Institute, Funchal, Portugal
2
INESC-ID, Instituto Superior Te´cnico, Lisbon, Portugal
3
UMa, Universidade da Madeira, Funchal, Portugal
The energy production through wind is determined by wind speed. In turn, the wind speed is easily influenced by obstacles and terrain. Thus, the energy generated from wind is uncertain and, as consequence, the operation for its integration in the electrical grid is affected. In order to minimize this problem, detailed schedule plans should be made by the grid regulators. So, an accurate forecasting of energy wind production is needed. The uncertain behavior of wind is the major obstacle for forecasting wind farm energy production and its further integration into the electrical grid. Therefore, accurate fore
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