An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization

Yin, Hao; Dong, Zhen; Chen, Yunlong; Ge, Jiafei; Lai, Loi Lei; Vaccaro, Alfredo; Meng, Anbo

doi:10.1016/j.enconman.2017.08.014

Cited by 95 publications

(28 citation statements)

References 42 publications

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“…(i) Initialization: the initial individuals and historical population in the BSA are randomly generated in the search-space, which is expressed mathematically as Equations (13) and (14).…”

Section: Backtracking Search Algorithmmentioning

confidence: 99%

“…Liu et al [13] developed a novel hybrid approach combining the secondary signal decomposition algorithm (SDA) with Elman neural network to predict wind speed, in the SDA, FEEMD algorithm was utilized to re-decompose the high frequency, namely detailed components obtained by WPD, into different components. In the work by the authors of [14], another SDA integrating empirical mode decomposition (EMD) and WPD was developed to decompose the wind speed for better WSP results. Peng et al [15] applied a compound WSF model combining the two-stage decomposition (TSD) with AdaBoost-extreme learning machine to make multistep WSF.…”

Section: Introductionmentioning

confidence: 99%

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A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Sun

2019

Energies

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Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

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“…(i) Initialization: the initial individuals and historical population in the BSA are randomly generated in the search-space, which is expressed mathematically as Equations (13) and (14).…”

Section: Backtracking Search Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Sun

2019

Energies

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“…In [11], a nonlinear autoregressive neural network model was established to forecast multi-step wind speed using direct and recursive strategies. A cross-optimization algorithm was used in [12] to train an extreme learning machine after the second decomposition of a wind power time series for wind power forecasting. Compared to similar models, the proposed method had several advantages.…”

Section: Introductionmentioning

confidence: 99%

Deterministic and Probabilistic Wind Power Forecasting Based on Bi-Level Convolutional Neural Network and Particle Swarm Optimization

et al. 2019

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The intermittency and uncertainty of wind power result in challenges for large-scale wind power integration. Accurate wind power prediction is becoming increasingly important for power system planning and operation. In this paper, a probabilistic interval prediction method for wind power based on deep learning and particle swarm optimization (PSO) is proposed. Variational mode decomposition (VMD) and phase space reconstruction are used to pre-process the original wind power data to obtain additional details and uncover hidden information in the data. Subsequently, a bi-level convolutional neural network is used to learn nonlinear features in the pre-processed wind power data for wind power forecasting. PSO is used to determine the uncertainty of the point-based wind power prediction and to obtain the probabilistic prediction interval of the wind power. Wind power data from a Chinese wind farm and modeled wind power data provided by the United States Renewable Energy Laboratory are used to conduct extensive tests of the proposed method. The results show that the proposed method has competitive advantages for the point-based and probabilistic interval prediction of wind power.

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“…In addition, there are still some deficiencies in the field of research, which need to be further studied. First, many literatures decompose training data and testing data together [31,32], which is not feasible in the process of real-time prediction. Regretfully, other literature does not clearly explain the construction process of the modeling data.…”

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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An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization

Cited by 95 publications

References 42 publications

A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Deterministic and Probabilistic Wind Power Forecasting Based on Bi-Level Convolutional Neural Network and Particle Swarm Optimization

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

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