Feature Extraction of Time Series Data for Wind Speed Power Generation

2016 ◽  
Author(s):  
Manju Khanna ◽  
N.K. Srinath ◽  
J.K. Mendiratta
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Feature Extraction ◽  
Wind Speed ◽  
Time Series Data ◽  
Series Data

scholarly journals The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

2018 ◽  
Vol 7 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Adekunlé Akim Salami ◽  
Ayité Sénah Akoda Ajavon ◽  
Mawugno Koffi Kodjo ◽  
Seydou Ouedraogo ◽  
Koffi-Sa Bédja
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Series Data ◽  
Empirical Method ◽  
Likelihood Method ◽  
Series Data ◽  
New Approach ◽  
Weibull Parameters ◽  
Distribution Histogram ◽  
Mean Wind Speed

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

Feature extraction from time series data

2009 ◽  
Vol 9 (s1) ◽  
pp. S99-S110 ◽  
Author(s):  
Durga Toshniwal
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Time Series Data ◽  
Series Data

Wavelet-Based Feature Extraction for Behavior Recognition in Mobile Robots

2010 ◽  
Author(s):  
Xin Jin ◽  
Kushal Mukherjee ◽  
Shalabh Gupta ◽  
Asok Ray
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Mobile Robots ◽  
Time Series Data ◽  
Piezoelectric Sensor ◽  
Relevant Information ◽  
Series Data ◽  
Test Bed ◽  
Behavior Recognition ◽  
Feature Extraction Method

This paper introduces a dynamic data-driven method for behavior recognition in mobile robots. The core concept of the paper is built upon the principle of symbolic dynamic filtering (SDF) that is used to extract relevant information in complex dynamical systems. The objective here is to identify the robot behavior from time-series data of piezoelectric sensor signals from the pressure sensitive floor in a laboratory environment. A symbolic feature extraction method is presented by partitioning of two-dimensional wavelet images of sensor time-series data. The K-nearest neighbors (k-NN) algorithm is used to identify the patterns extracted by SDF. The proposed method is validated by experimentation on a networked robotics test bed to detect and identify the type and motion profile of mobile robots.

A Hybrid Wind Speed Prediction Approach Based on Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks for Digital Twin

2020 ◽  
Author(s):  
Weifei Hu ◽  
Yihan He ◽  
Zhenyu Liu ◽  
Jianrong Tan ◽  
Ming Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Wind Speed ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Digital Twin ◽  
Mode Decomposition

Abstract Precise time series prediction serves as an important role in constructing a Digital Twin (DT). The various internal and external interferences result in highly non-linear and stochastic time series data sampled from real situations. Although artificial Neural Networks (ANNs) are often used to forecast time series for their strong self-learning and nonlinear fitting capabilities, it is a challenging and time-consuming task to obtain the optimal ANN architecture. This paper proposes a hybrid time series prediction model based on ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM) neural networks, and Bayesian optimization (BO). To improve the predictability of stochastic and nonstationary time series, the EEMD method is implemented to decompose the original time series into several components, each of which is composed of single-frequency and stationary signal, and a residual signal. The decomposed signals are used to train the BO-LSTM neural networks, in which the hyper-parameters of the LSTM neural networks are fine-tuned by the BO algorithm. The following time series data are predicted by summating all the predictions of the decomposed signals based on the trained neural networks. To evaluate the performance of the proposed hybrid method (EEMD-BO-LSTM), this paper conducts a case study of wind speed time series prediction and has a comprehensive comparison between the proposed method and other approaches including the persistence model, ARIMA, LSTM neural networks, B0-LSTM neural networks, and EEMD-LSTM neural networks. Results show an improved prediction accuracy using the EEMD-BO-LSTM method by multiple accuracy metrics.

scholarly journals Research on Combined Model Based on Multi-Objective Optimization and Application in Wind Speed Forecast

2019 ◽  
Vol 9 (3) ◽  
pp. 423 ◽  
Author(s):  
Shenghui Zhang ◽  
Yuewei Liu ◽  
Jianzhou Wang ◽  
Chen Wang
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Linear Models ◽  
Time Series Data ◽  
Mean Absolute Percentage Error ◽  
Series Data ◽  
Percentage Error ◽  
Combined Model ◽  
Absolute Percentage Error ◽  
Model Based

Wind power is an important part of a power system, and its use has been rapidly increasing as compared with fossil energy. However, due to the intermittence and randomness of wind speed, system operators and researchers urgently need to find more reliable wind-speed prediction methods. It was found that the time series of wind speed not only has linear characteristics, but also nonlinear. In addition, most methods only consider one criterion or rule (stability or accuracy), or one objective function, which can lead to poor forecasting results. So, wind-speed forecasting is still a difficult and challenging problem. The existing forecasting models based on combination-model theory can adapt to some time-series data and overcome the shortcomings of the single model, which achieves poor accuracy and instability. In this paper, a combined forecasting model based on data preprocessing, a nondominated sorting genetic algorithm (NSGA-III) with three objective functions and four models (two hybrid nonlinear models and two linear models) is proposed and was successfully applied to forecasting wind speed, which not only overcomes the issue of forecasting accuracy, but also solves the difficulties of forecasting stability. The experimental results show that the stability and accuracy of the proposed combined model are better than the single models, improving the mean absolute percentage error (MAPE) range from 0.007% to 2.31%, and the standard deviation mean absolute percentage error (STDMAPE) range from 0.0044 to 0.3497.

Feature Extraction of Motion from Time-series Data by using Attractors

2012 ◽  
Vol 132 (6) ◽  
pp. 975-982
Author(s):  
Takuma Akiduki ◽  
Zhong Zhang ◽  
Takashi Imamura ◽  
Tetsuo Miyake
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Time Series Data ◽  
Series Data

Fusion Process Neural Networks Classifier Oriented Time Series

2019 ◽  
Vol 16 (10) ◽  
pp. 4059-4063
Author(s):  
Ge Li ◽  
Hu Jing ◽  
Chen Guangsheng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Feature Extraction ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Series Data ◽  
Time Varying ◽  
Synthetic Control ◽  
Classification Feature

Based on the consideration of complementary advantages, different wavelet, fractal and statistical methods are integrated to complete the classification feature extraction of time series. Combined with the advantage of process neural networks that processing time-varying information, we propose a fusion classifier with process neural network oriented time series. Be taking advantage of the multi-fractal processing nonlinear feature of time series data classification, the strong adaptability of the wavelet technique for time series data and the effect of statistical features on the classification of time series data, we can achieve the classification feature extraction of time series. Additionally, using time-varying input characteristics of process neural networks, the pattern matching of timevarying input information and space-time aggregation operation is realized. The feature extraction of time series with the above three methods is fused to the distance calculation between time-varying inputs and cluster space in process neural networks. We provide the process neural network fusion to the learning algorithm and optimize the calculation process of the time series classifier. Finally, we report the performance of our classification method using Synthetic Control Charts data from the UCI dataset and illustrate the advantage and validity of the proposed method.

scholarly journals Feature extraction for time-series data: An artificial neural network evolutionary training model for the management of mountainous watersheds

2009 ◽  
Vol 73 (1-3) ◽  
pp. 49-59 ◽  
Author(s):  
Thomas J. Glezakos ◽  
Theodore A. Tsiligiridis ◽  
Lazaros S. Iliadis ◽  
Constantine P. Yialouris ◽  
Fotis P. Maris ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Time Series Data ◽  
Training Model ◽  
Series Data ◽  
Artificial Neural ◽  
Mountainous Watersheds
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