Fractal order evidences in wind speed time series

Fortuna, Luigi; Nunnari, Silvia; Guariso, Giorgio

doi:10.1109/icfda.2014.6967450

Cited by 14 publications

(17 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Depending on the data set, an analysis on the wind speed characteristics can take place. For instance, in (Fortuna et al [20], 2014), the authors provide a fractal analysis on wind speed observations. Exploitable information can be derived for such analysis for further modeling.…”

Section: Motivation and State-of-the-artmentioning

confidence: 99%

A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence

2019

View full text Add to dashboard Cite

Wind speed forecasting is an important element for the further development of offshore wind turbines. Due to its importance, many researchers have proposed different models for wind speed forecasting that differ in terms of the time-horizon of the forecast, types and number of inputs, complexity, structure, and others. Wind speed series present high nonlinearity and volatilities, and thus an effective model should successfully deal with those features. An approach to deal with the nonlinearities and volatilities is to utilize a time series processing technique such as the wavelet transform. In the present paper, an ensemble data-driven short-term wind speed forecasting model is developed, tested and applied. The term “ensemble” refers to the combination of two different predictors that run in parallel and the prediction is obtained by the predictor that leads to the lowest error. The proposed model utilizes the wavelet transform and is compared with other models that have been presented in the related literature and outperforms their accuracy. The proposed forecasting model can be used effectively for 1 min and 10 min ahead horizon wind speed predictions.

show abstract

Section: Motivation and State-of-the-artmentioning

confidence: 99%

A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence

2019

View full text Add to dashboard Cite

show abstract

“…Telesca and Lovallo [18] found that most of the multifractality of the wind speed series recorded at several heights from the ground from 50m to 213m was due to the different long-range correlations in small and large speed fluctuations. Fortuna et al [19] applied the MFDFA to several hourly wind speed series in Italy and USA and found that the multifractal width ranged in a quite close interval of values between 0.39 and 0.59. De Figueiredo et al [20] found that the mean and the maximum of four wind speed time series in Brazil were all persistent, but the maximum was more multifractal than the mean.…”

mentioning

confidence: 99%

Multifractal analysis of the time series of daily means of wind speed in complex regions

Laib

Golay

Telesca

et al. 2018

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

In this paper, we applied the multifractal detrended fluctuation analysis to the daily means of wind speed measured by 119 weather stations distributed over the territory of Switzerland. The analysis was focused on the inner time fluctuations of wind speed, which could be more linked with the local conditions of the highly varying topography of Switzerland. Our findings point out to a persistent behaviour of all the measured wind speed series (indicated by a Hurst exponent significantly larger than 0.5), and to a high multifractality degree indicating a relative dominance of the large fluctuations in the dynamics of wind speed, especially in the Swiss plateau, which is comprised between the Jura and Alp mountain ranges. The study represents a contribution to the understanding of the dynamical mechanisms of wind speed variability in mountainous regions.

show abstract

“…Recently, Cadenas et al [24] compared the predictive capacity of a univariate ARIMA model for wind speed against a nonlinear autoregressive exogenous model (NARX) model using as input the wind direction, temperature, pressure, solar radiation, relative humidity and speed of the wind. The main assumption of the statistical methods is that these assume a family of underlying stochastic processes that are generally stationary, linear and homoscedastic, but most of the time series analyzed do not comply with these restrictions, since they have characteristics of heteroscedasticity and present long-term dependencies [7,8].…”

Section: Methodsmentioning

confidence: 99%

Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory

et al. 2018

View full text Add to dashboard Cite

Wind power generation has presented an important development around the world. However, its integration into electrical systems presents numerous challenges due to the variable nature of the wind. Therefore, to maintain an economical and reliable electricity supply, it is necessary to accurately predict wind generation. The Wind Power Prediction Tool (WPPT) has been proposed to solve this task using the power curve associated with a wind farm. Recurrent Neural Networks (RNNs) model complex non-linear relationships without requiring explicit mathematical expressions that relate the variables involved. In particular, two types of RNN, Long Short-Term Memory (LSTM) and Echo State Network (ESN), have shown good results in time series forecasting. In this work, we present an LSTM+ESN architecture that combines the characteristics of both networks. An architecture similar to an ESN is proposed, but using LSTM blocks as units in the hidden layer. The training process of this network has two key stages: (i) the hidden layer is trained with a descending gradient method online using one epoch; (ii) the output layer is adjusted with a regularized regression. In particular, the case is proposed where Step (i) is used as a target for the input signal, in order to extract characteristics automatically as the autoencoder approach; and in the second stage (ii), a quantile regression is used in order to obtain a robust estimate of the expected target. The experimental results show that LSTM+ESN using the autoencoder and quantile regression outperforms the WPPT model in all global metrics used.

show abstract

Fractal order evidences in wind speed time series

Cited by 14 publications

References 14 publications

A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence

A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence

Multifractal analysis of the time series of daily means of wind speed in complex regions

Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory

Contact Info

Product

Resources

About