Abstract
Wind is a physical phenomenon with uncertainties in several temporal scales, in addition, measured wind time series have noise superimposed on them. These time series are the basis for forecasting methods. This paper studied the application of the wavelet transform to three forecasting methods, namely, stochastic, neural network, and fuzzy, and six wavelet families. Wind speed time series were first filtered to eliminate the high-frequency component using wavelet filters and then the different forecasting methods were applied to the filtered time series. All methods showed important improvements when the wavelet filter was applied. It is important to note that the application of the wavelet technique requires a deep study of the time series in order to select the appropriate family and filter level. The best results were obtained with an optimal filtering level and improper selection may significantly affect the accuracy of the results.
Highlights
When the penetration of wind power into the network reaches a certain level, system operators have difficulties in balancing generation with demand
The wavelet filter was applied to several forecasting methods, namely regression, 3.2
The wavelet filter was applied to several forecasting methods: regression, neural network, and fuzzy models
Summary
When the penetration of wind power into the network reaches a certain level, system operators have difficulties in balancing generation with demand. Most authors [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31] have used wavelets to decompose the time series into sub-series, called approximation and details; applied the forecasting method to each sub-series, and summarized the forecasting results to obtain the final solution The advantage of this method comes from the sub-series having an improved performance with respect to the original series. These works used the wavelet transform as an auxiliary technique and did not study them at sufficient depth
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