Abstract
This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: (1) As a single time series containing all measurements, and (2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 h in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-squares support vector machines, empirical mode decomposition, and the wavelet transform. Moreover, the traditional and simple auto-regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a matlab implementation showed that the least-squares support vector machine using data as a single time series outperformed the other combinations, obtaining the least root mean square error (RMSE).
Highlights
At the end of the 20th century, a change in the trends of energy production began, promoted by concerns about pollution, climate change and the dependence of some countries on the importation of hydrocarbons
The prediction methods implemented in the platform are defined and description is made of the non-parametric techniques that are used: Wavelet transform (WT), Empirical mode decomposition (EMD) and Least-squares support vector machine (LSSVM)
The metric used to measure the performance of each prediction technique is the root mean square error (RMSE)
Summary
At the end of the 20th century, a change in the trends of energy production began, promoted by concerns about pollution, climate change and the dependence of some countries on the importation of hydrocarbons. In this context, great interest is concentrated in the opportunities offered by renewable technologies as a source of supply in non-interconnected areas and as part of the integrated generation system that participates in the pool in the wholesale market [1,2]. Energy policies in many countries encourage the implementation, expansion and interconnection of alternative renewable energy sources such as photovoltaics, solar thermal, wind, geothermal, hydroelectric, wave, tidal and biofuel. Wind energy production is the fastest-growing energy generation technology, with a connected capacity in the world of
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