A Short-Term Wind Speed Forecasting Model Based on EMD/CEEMD and ARIMA-SVM Algorithms

Abstract

In order to ensure the driving safety of vehicles in windy environments, a wind monitoring and warning system is widely used, in which a wind speed prediction algorithm with better stability and sufficient accuracy is one of the key factors to ensure the smooth operation of the system. In this paper, a novel short-term wind speed forecasting model, combining complementary ensemble empirical mode decomposition (CEEMD), auto-regressive integrated moving average (ARIMA), and support vector machine (SVM) technology, is proposed. Firstly, EMD and CEEMD are used to decompose the measured wind speed sequence into a finite number of intrinsic mode functions (IMFs) and a decomposed residual. Each of the IMF subseries has better linear characteristics. The ARIMA algorithm is adopted to predict each of the subseries. Then, a new subseries is reconstructed using the sum of the predicted errors of all subseries. The high nonlinear features of the reconstructed error subseries are modeled using SVM, which is suitable to process nonlinear data. Finally, the superposition of all prediction results is performed to obtain the final predicted wind speed. To verify the stability and accuracy of the model, two typhoon datasets, measured from the south coast of China, are used to test the proposed methods. The results show that the proposed hybrid model has a better predictive ability than single models and other combined models. The root mean squared errors (RMSEs) of the hybrid model for the three wind speed datasets are 0.839, 0.529, and 0.377, respectively. The combination of CEEMD with ARIMA contributes most of the prediction performance to the hybrid model. It is feasible to apply the hybrid model to wind speed prediction.

An Ensemble Approach to Short-Term Wind Speed Predictions Using Stochastic Methods, Wavelets and Gradient Boosting Decision Trees

“…Wind speed is chaotically intermittent and is often characterised by inherent linear and nonlinear patterns as well as nonstationary behaviour; thus, it is generally difficult to predict it accurately and efficiently using a single linear or nonlinear model [25,46,53,54]. We suggest combining WT, ARIMA, and XGBoost via SVR to predict high-resolution short-term wind speeds.…”

“…ARMA models are parametric models for stationary univariate time series and were discovered and popularised by [29]. In addition to their simplicity and robustness, ARMA models are advantageous in forecasting, as they capture the linear component excellently [25,29,30,44,51,53,54]. Hence, these are the most popular forecasting approaches.…”

Performance enhancement of short-term wind speed forecasting model using Realtime data

“…With the explosion of interest in data science over the past few years, statistical models have come into greater focus [17]. Machine learning (ML) methods [18], such as artificial neural networks (ANN) [19], support vector machines (SVM) [20], extreme learning machines (ELM) [21], and deep learning (DL) [22], are developing remarkably quickly. In this paper, we are exploiting the power of machine learning in short-term wind speed forecasting and propose an ensemble L-LG-S model for accurate predictions.…”

Noise Reduction Based on a CEEMD-WPT Crack Acoustic Emission Dataset

“…Jing et al [21] designed a noise reduction method combining VMD and fuzzy entropy to remove non-linear noise in coal mine monitoring systems. Chen et al [22] combined CEEMD, Auto-Regressive Integrated Moving Average (ARIMA), and SVM to design a wind speed prediction model. The model predicted and summarized the CEEMD component and the reconstructed SVM decomposition component of the signal, respectively, with the predicted wind speed.…”