This paper deals with the clustering of daily wind speed time series based on two features, namely the daily average wind speed and the corresponding degree of fluctuation. Daily values of the feature pairs are first classified by means of the fuzzy c-means unsupervised clustering algorithm and then results are used to train a supervised MLP neural network classifier. It is shown that associating to a true wind speed time series a time series of classes allows performing some useful statistics. Further, the problem of predicting the class of daily wind speed 1-step ahead is addressed by using both the Hidden Markov Models (HMM) and the Non-linear Auto-Regressive (NAR) approaches. The performances of the considered class prediction models are finally assessed in terms of True Positive rate (TPR) and True Negative rate (TNR), also in comparison with the persistent model.

One Day Ahead Prediction of Wind Speed Class by Statistical Models

GUARISO, GIORGIO;
2016-01-01

Abstract

This paper deals with the clustering of daily wind speed time series based on two features, namely the daily average wind speed and the corresponding degree of fluctuation. Daily values of the feature pairs are first classified by means of the fuzzy c-means unsupervised clustering algorithm and then results are used to train a supervised MLP neural network classifier. It is shown that associating to a true wind speed time series a time series of classes allows performing some useful statistics. Further, the problem of predicting the class of daily wind speed 1-step ahead is addressed by using both the Hidden Markov Models (HMM) and the Non-linear Auto-Regressive (NAR) approaches. The performances of the considered class prediction models are finally assessed in terms of True Positive rate (TPR) and True Negative rate (TNR), also in comparison with the persistent model.
2016
wind speed; time series clustering; fcm algorithm; HMM models; NARmodels
File in questo prodotto:
File Dimensione Formato  
One day ahead predicion IJRER.pdf

accesso aperto

: Publisher’s version
Dimensione 752.73 kB
Formato Adobe PDF
752.73 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1000112
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 8
social impact