Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

Fan, Guo‐Feng; Qing, Shan; Wang, Hua; Hong, Wei-Chiang; Li, Hongjuan

doi:10.3390/en6041887

Cited by 112 publications

(68 citation statements)

References 25 publications

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“…SRM minimizes an upper bound on the expected risk, whereas ERM minimizes the error on training data. In [29], a SVM model hybridized with the empirical mode decomposition (EMD) method and auto regression (AR) was implemented for electrical load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

et al. 2014

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A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP). A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM) with Wavelet Decomposition (WD) were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN)-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

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Section: Introductionmentioning

confidence: 99%

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

et al. 2014

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“…The technique has found applications in many branches of engineering, e.g. on electric load forecasting [13], and detailed theoretical development of EMD can be found in [14]. Central to the EMD approach is a sifting process which, for the problem in hand, allows P G (t) to be decomposed into a family of intrinsic mode functions (IMF), denoted herewith as c i (t), i = 1,. .…”

Section: General Description Of the Proposed Design Methodologymentioning

confidence: 99%

Determination of optimal supercapacitor-lead-acid battery energy storage capacity for smoothing wind power using empirical mode decomposition and neural network

Yuan

Sun

et al. 2015

Electric Power Systems Research

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“…So as to improve the performance of single-model-based prediction models, a novel framework based on decomposition algorithm has been introduced for time series prediction [18]. Multiple decomposition methods have been put forward to analyze time series, thus forming the hybrid prediction models [19]. The subsequences obtained by the decomposition algorithms are much easier to predict than the original time series, which brings forward a new means of predicting nonlinear and non-stationary time series [20].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction

Ren

2019

Symmetry

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The prediction of chaotic time series has been a popular research field in recent years. Due to the strong non-stationary and high complexity of the chaotic time series, it is difficult to directly analyze and predict depending on a single model, so the hybrid prediction model has become a promising and favorable alternative. In this paper, we put forward a novel hybrid model based on a two-layer decomposition approach and an optimized back propagation neural network (BPNN). The two-layer decomposition approach is proposed to obtain comprehensive information of the chaotic time series, which is composed of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD). The VMD algorithm is used for further decomposition of the high frequency subsequences obtained by CEEMDAN, after which the prediction performance is significantly improved. We then use the BPNN optimized by a firefly algorithm (FA) for prediction. The experimental results indicate that the two-layer decomposition approach is superior to other competing approaches in terms of four evaluation indexes in one-step and multi-step ahead predictions. The proposed hybrid model has a good prospect in the prediction of chaotic time series.

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Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

Cited by 112 publications

References 25 publications

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

Determination of optimal supercapacitor-lead-acid battery energy storage capacity for smoothing wind power using empirical mode decomposition and neural network

A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction

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