Chaotic characteristic analysis of short-term wind speed time series with different time scales

2019 ◽  
pp. 1-15
Author(s):  
Zhongda Tian
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Scales ◽  
Characteristic Analysis ◽  
Short Term ◽  
Chaotic Characteristic ◽  
Different Time Scales

Preliminary Research of Chaotic Characteristics and Prediction of Short-Term Wind Speed Time Series

2020 ◽  
Vol 30 (12) ◽  
pp. 2050176
Author(s):  
Zhongda Tian
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Scales ◽  
Prediction Method ◽  
Test Method ◽  
Echo State Network ◽  
Maximum Lyapunov Exponent ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Different Time Scales

Short-term wind speed prediction has its special significance in wind power industry. However, due to the characteristics of the wind system itself, it is not easy to predict the short-term wind speed accurately. In order to solve the problem, this paper studies the chaotic characteristics and prediction of short-term wind speed time series. The short-term wind speed data at four time scales are collected as the research object. The predictability of short-term wind speed time series is determined by the Hurst exponent. The chaotic characteristics of short-time wind speed at different time scales are analyzed by the 0–1 test method for chaos and the maximum Lyapunov exponent method. The results show that the short-term wind speed time series has chaotic characteristics at different time scales. The phase-space reconstruction technology is introduced; delay time is determined by the C–C method; embedding dimension is obtained by the G–P method. Echo state network is improved to suppress the influence of input noise on prediction performance. At the same time, an improved grey Wolf optimization algorithm is proposed to optimize the parameters of reserve pool of the echo state network. The results of a case study show that, compared with state-of-the-art methods, the proposed prediction method improves the prediction accuracy and reduces the predictive errors.

scholarly journals Characterizing ecosystem-atmosphere interactions from short to interannual time scales

2007 ◽  
Vol 4 (3) ◽  
pp. 1405-1435
Author(s):  
M. D. Mahecha ◽  
M. Reichstein ◽  
H. Lange ◽  
N. Carvalhais ◽  
C. Bernhofer ◽  
...  
Keyword(s):  
Time Series ◽  
Time Scales ◽  
System Analysis ◽  
Singular System ◽  
Low Frequency ◽  
Data Adaptive ◽  
Effective Signal ◽  
Observation Space ◽  
Different Time Scales ◽  
Observation Period

Abstract. Characterizing ecosystem-atmosphere interactions in terms of carbon and water exchange on different time scales is considered a major challenge in terrestrial biogeochemical cycle research. The respective time series are now partly comprising an observation period of one decade. In this study, we explored whether the observation period is already sufficient to detect cross relationships of the variables beyond the annual cycle as they are expected from comparable studies in climatology. We explored the potential of Singular System Analysis (SSA) to extract arbitrary kinds of oscillatory patterns. The method is completely data adaptive and performs an effective signal to noise separation. We found that most observations (NEE, GPP, Reco, VPD, LE, H, u, P) were influenced significantly by low frequency components (interannual variability). Furthermore we extracted a set of nonlinear relationships and found clear annual hysteresis effects except for the NEE-Rg relationship which turned out to be the sole linear relationship in the observation space. SSA provides a new tool to investigate these phenomena explicitly on different time scales. Furthermore, we showed that SSA has great potential for eddy covariance data processing since it can be applied as novel gap filling approach relying on the temporal time series structure only.

scholarly journals Short-Term Variations of LBV’s

1999 ◽  
Vol 169 ◽  
pp. 251-254
Author(s):  
Otmar Stahl
Keyword(s):  
Time Scales ◽  
Monitoring Program ◽  
Short Term ◽  
Star Group ◽  
Different Time Scales

LBV’s are variable on many different time scales from weeks to years or even decades. The typical LBV variations on timescales of years or longer are covered in the paper by Humphreys (this volume). Here I discuss variations of LBV’s on timescales from weeks to years.Most classes of stars are little studied on these timescales. This also holds true for LBV’s. Therefore the hot star group of the Landessternwarte Heidelberg Started a monitoring program of a few bright LBV’s (and other objects) using the 70cm-telescopes at the Landessternwarte and later the ESO 50cm-telescope at La Silla. For more details about the project see the paper by Kaufer (this volume).

Short-term wind speed forecasting using ARIMA model

Energetika ◽  
2016 ◽  
Vol 62 (1-2) ◽  
Author(s):  
Ernesta Grigonytė ◽  
Eglė Butkevičiūtė
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Moving Average ◽  
Arima Model ◽  
Model Structure ◽  
Optimal Model ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Auto Correlation ◽  
Auto Correlation Function

The massive integration of wind power into the power system increasingly calls for better short-term wind speed forecasting which helps transmission system operators to balance the power systems with less reserve capacities. The  time series analysis methods are often used to analyze the  wind speed variability. The  time series are defined as a sequence of observations ordered in time. Statistical methods described in this paper are based on the prediction of future wind speed data depending on the historical observations. This allows us to find a sufficiently good model for the wind speed prediction. The paper addresses a short-term wind speed forecasting ARIMA (Autoregressive Integrated Moving Average) model. This method was applied for a number of different prediction problems, including the short term wind speed forecasts. It is seen as an early time series methodology with well-known limitations in wind speed forecasting, mainly because of insufficient accuracies of the hourly forecasts for the second half of the day-ahead forecasting period. The authors attempt to find the maximum effectiveness of the model aiming to find: (1) how the identification of the optimal model structure improves the forecasting results and (2) what accuracy increase can be gained by reidentification of the structure for a new wind weather season. Both historical and synthetic wind speed data representing the sample locality in the Baltic region were used to run the model. The model structure is defined by rows p, d, q and length of retrospective data period. The structure parameters p (Autoregressive component, AR) and q (Moving Average component, MA) were determined by the Partial Auto-Correlation Function (PACF) and Auto-Correlation Function (ACF), respectively. The model’s forecasting accuracy is based on the root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE). The results allowed to establish the optimal model structure and the length of the input/retrospective period. The  quantitative study revealed that identification of the  optimal model structure gives significant accuracy improvement against casual structures for 6–8 h forecast lead time, but a season-specific structure is not appropriate for the entire year period. Based on the conducted calculations, we propose to couple the ARIMA model with any more effective method into a hybrid model.

scholarly journals The Empirical Adaptive Wavelet Decomposition (EAWD): An adaptive decomposition for the variability analysis of observation time series in atmospheric science

2022 ◽  
Author(s):  
Olivier Delage ◽  
Thierry Portafaix ◽  
Hassan Bencherif ◽  
Alain Bourdier ◽  
Emma Lagracie
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Observation Time ◽  
Stationary Time Series ◽  
Original Time Series ◽  
Adaptive Wavelet ◽  
Time Frequency ◽  
Variability Analysis ◽  
Original Time ◽  
Different Time Scales

Abstract. Most observational data sequences in geophysics can be interpreted as resulting from the interaction of several physical processes at several time and space scales. As a consequence, measurements time series have often characteristics of non-linearity and non-stationarity and thereby exhibit strong fluctuations at different time-scales. The variability analysis of a time series consists in decomposing it into several mode of variability, each mode representing the fluctuations of the original time series at a specific time-scale. Such a decomposition enables to obtain a time-frequency representation of the original time series and turns out to be very useful to estimate the dimensionality of the underlying dynamics. Decomposition techniques very well suited to non-linear and non-stationary time series have recently been developed in the literature. Among the most widely used of these technics are the empirical mode decomposition (EMD) and the empirical wavelet transformation (EWT). The purpose of this paper is to present a new adaptive filtering method that combines the advantages of the EMD and EWT technics, while remaining close to the dynamics of the original signal made of atmospheric observations, which means reconstructing as close as possible to the original time series, while preserving its variability at different time scales.

scholarly journals Quantitative Analysis of the Impact of Meteorological Factors on Reference Evapotranspiration Changes in Beijing, 1958–2017

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2263 ◽  
Author(s):  
Wenhui Liu ◽  
Baozhong Zhang ◽  
Songjun Han
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Time Scales ◽  
Reference Evapotranspiration ◽  
Meteorological Factors ◽  
Sensitivity Coefficient ◽  
Sensitivity Factor ◽  
Net Radiation ◽  
Annual Time ◽  
Different Time Scales

The effects of meteorological factors on reference evapotranspiration (ET0) are variable on different time scales, although research tends to focus only on certain time scales. Therefore, using the meteorological data from 1958 to 2017 of Beijing, China, ET0 values over the last 60 years were calculated using Penman–Monteith method. The variation in ET0 values was thus analyzed against four meteorological factors over different time scales. The sensitivity of ET0 to these factors was assessed using a sensitivity coefficient, while the contribution of each factor to ET0 change was quantified by combining this sensitivity coefficient with the factor’s relative change rate over multiple time scales. The results showed that the sensitivity coefficient of relative humidity over different time scales were all negative, while the sensitivity coefficients of net radiation, temperature and wind speed were mostly positive. The main sensitivity factors of ET0 on different time scales varied. On annual time scales, the main factors were relative humidity and temperature. Over annual time scales, relative humidity and net radiation alternated as the main sensitivity factor; while over interannual time scales, the most sensitive factor was relative humidity during 1958–1979 and net radiation thereafter. The contribution of these four meteorological factors to ET0 also fluctuated greatly on intra-annual time scales. On daily time scales, the contributions of temperature and wind speed at the start and end of the year were large, while net radiation and relative humidity were dominant mid-year. On monthly to seasonal time scales, the contributions of these four meteorological factors to ET0 were notable. The contribution of relative humidity was largest in spring and autumn; net radiation was dominant in summer, while temperature and wind speed were dominant in winter. This research on the temporal variability of ET0 response factors is of great significance for understanding regional climate change.

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