Short-term wind speed prediction using an extreme learning machine model with error correction

Wang, Lili; Li, Xin; Bai, Yulong

doi:10.1016/j.enconman.2018.02.015

Cited by 133 publications

(40 citation statements)

References 36 publications

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“…This approach decomposes non-stationary sensor data into stationary data and retains the structure of the raw data. Therefore, to solve the problems noted above, some signal processing methods, such as empirical mode decomposition (EMD), complementary ensemble EMD (CEEMD), ensemble EMD (EEMD), variational mode decomposition (VMD) and wavelet transform (WT), have been widely applied to recursively decompose data into different intrinsic modes and improve the effectiveness of outlier detection [24]- [26]. To a large extent, signal processing methods have a limited capacity to improve the performance and accuracy of a detection model.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Outlier Detection and Processing Approach Towards Time Series Sensor Data

Zhang

Wang

2019

IEEE Access

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The intelligent environment monitoring network, as the foundation of ecosystem research, has rapidly developed with the ever-growing Internet of Things (IoT). IoT-networked sensors deployed to monitor ecosystems generate copious sensor data characterized by nonstationarity and nonlinearity such that outlier detection remains a source of concern. Most outlier detection models involve hypothesis tests based on setting outlier threshold values. However, signal decomposition describes stationary and nonstationary relationships sensor data. Therefore, this paper proposes a three-level hybrid model based on the median filter (MF), empirical mode decomposition (EMD), classification and regression tree (CART), autoregression (AR) and exponential weighted moving average (EWMA) methods called MF-EMD-CART-AR-EWMA to detect outliers in sensor data. The first-level performance is compared to that of the Butterworth filter, FIR filter, moving average filter, wavelet filter and Wiener filter. The second-level prediction performance is compared to support vector regression (SVR), K-nearest neighbor (KNN), CART, complementary ensemble EEMD with CART and AR (EEMD-CART-AR) and ensemble CEEMD with CART and AR (CEEMD-CART-AR) methods. Finally, EWMA is compared to Cumulative Sum Control Chart (CUSUM) and Shewhart control charts. The proposed hybrid model was evaluated with a real dataset from the hydrometeorological observation network in the Heihe River Basin, yielding experimental results with better generalization ability and higher accuracy than the compared models, and providing extremely effective detection of minor outliers in predicted values. This paper provides valuable insight and a promising reference for outlier detection involving sensor data and presents a new perspective for detecting outliers.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Outlier Detection and Processing Approach Towards Time Series Sensor Data

Zhang

Wang

2019

IEEE Access

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“…Autoregressive (AR) method (Qiao et al, 2015), autoregressive moving average (ARMA) method (Askari et al, 2013;Erdem and Shi, 2011), and autoregressive integrated moving average (ARIMA) method (Cadenas et al, 2016) are common linear methods. The nonlinear method of short-term wind speed include support vector machine (SVM) model (Du et al, 2017;Gani et al, 2016), least square support vector machine (LSSVM) model (Ren et al, 2016;Sun et al, 2015), artificial neural network (Elman neural network (Yu et al, 2017(Yu et al, , 2018, echo state network (ESN; Sun and Liu, 2016), fuzzy neural network (Dong et al, 2017;Ma et al, 2017), radial basis function (RBF) neural network (Chang et al, 2017;Kirbas and Kerem, 2016), extreme learning machine (Nikolic et al, 2016;Tian et al, 2018b;Wang et al, 2018). Although these single forecasting or prediction models have been widely used, they all have their own limitations.…”

Section: Introductionmentioning

confidence: 99%

Short-term wind speed forecasting based on autoregressive moving average with echo state network compensation

2019

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In order to improve the forecasting accuracy of short-term wind speed, a forecasting method based on autoregressive moving average with echo state network compensation is proposed in this article. First, the linear and nonlinear characteristics of short-term wind speed can be determined by Brock–Dechert–Scheinkman statistics method. Then, autoregressive moving average model is used for modeling and to forecast the linear component of short-term wind speed. The linear component of short-term wind speed sequence is obtained. Artificial bee colony algorithm–optimized echo state network model is used as the forecasting model of forecasting error sequences with the nonlinear characteristic. Finally, the final forecasting value is obtained by adding forecasting values of autoregressive moving average model and forecasting error values of echo state network model. k-fold cross-validation is used to improve the generalization ability of the forecasting model. The simulation comparison results show that the proposed forecasting method has higher prediction accuracy with the smaller prediction error. The forecasting indicators are also better than other forecasting methods.

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“…To this end, it is of great significance to develop optimization methods for promoting prediction performance. The existing optimization algorithms have three main aspects, including signal processing techniques [22][23][24], parameters optimization techniques [25,26] and error correction techniques [27,28]. As shown in Table 1, it is a summary of the above-mentioned and related algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the decomposition algorithm mentioned above, error correction is also a method to improve the performance of the prediction model [30]. In [28], an error correction model based on ICEEMDAN and ARIMA algorithm is proposed to promote the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Online Hybrid Model for Temperature Prediction of Wind Turbine Gearbox Components

et al. 2019

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Condition monitoring can improve the reliability of wind turbines, which can effectively reduce operation and maintenance costs. The temperature prediction model of wind turbine gearbox components is of great significance for monitoring the operation status of the gearbox. However, the complex operating conditions of wind turbines pose grand challenges to predict the temperature of gearbox components. In this study, an online hybrid model based on a long short term memory (LSTM) neural network and adaptive error correction (LSTM-AEC) using simple-variable data is proposed. In the proposed model, a more suitable deep learning approach for time series, LSTM algorithm, is applied to realize the preliminary prediction of temperature, which has a stronger ability to capture the non-stationary and non-linear characteristics of gearbox components temperature series. In order to enhance the performance of the LSTM prediction model, the adaptive error correction model based on the variational mode decomposition (VMD) algorithm is developed, where the VMD algorithm can effectively solve the prediction difficulty issue caused by the non-stationary, high-frequency and chaotic characteristics of error series. To apply the hybrid model to the online prediction process, a real-time rolling data decomposition process based on VMD algorithm is proposed. With aims to validate the effectiveness of the hybrid model proposed in this paper, several traditional models are introduced for comparative analysis. The experimental results show that the hybrid model has better prediction performance than other comparative models.

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Short-term wind speed prediction using an extreme learning machine model with error correction

Cited by 133 publications

References 36 publications

An Adaptive Outlier Detection and Processing Approach Towards Time Series Sensor Data

An Adaptive Outlier Detection and Processing Approach Towards Time Series Sensor Data

Short-term wind speed forecasting based on autoregressive moving average with echo state network compensation

An Online Hybrid Model for Temperature Prediction of Wind Turbine Gearbox Components

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