A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine

Zhang, Dan; Peng, Xiangang; Pan, Keda; Liu, Yi

doi:10.1016/j.enconman.2018.10.089

Cited by 124 publications

(33 citation statements)

References 35 publications

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“…Therefore, the output weights between hidden and output layers are determined as finding the least square solution to the given linear system. Other variants of ELM utilized in wind speed and power forecasting include Hysteresis ELM (HELM) [61], Online Sequential ELM (OSELM) [62], Stacked ELM (SELM) [63], Regularized ELM (RELM) [64], and Weighted RELM (WRELM) [65]. Reference [66] discussed in detail the trends in ELM.…”

Section: ) Artificial Intelligence/ Machine Learning Methodsmentioning

confidence: 99%

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

2019

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The intermittent nature of wind energy raised multiple challenges to the power systems and is the biggest challenge to declare wind energy a reliable source. One solution to overcome this problem is wind energy forecasting. A precise forecast can help to develop appropriate incentives and wellfunctioning electric markets. The paper presents a comprehensive review of existing research and current developments in deterministic wind speed and power forecasting. Firstly, we categorize wind forecasting methods into four broader classifications: input data, timescales , power output, and forecasting method. Secondly, the performance of wind speed and power forecasting models is evaluated based on 634 accuracy tests reported in twenty-eight published articles covering fifty locations of ten countries. From the analysis, the most significant errors were witnessed for the physical models, whereas the hybrid models showed the best performance. Although, the physical models have a large normalized root mean square error values but have small volatility. The hybrid models perform best for every time horizon. However, the errors almost doubled at the medium-term forecast from its initial value. The statistical models showed better performance than artificial intelligence models only in the very short term forecast. Overall, we observed the increase in the performance of forecasting models during the last ten years such that the normalized mean absolute error and normalized root mean square error values reduced to about half the initial values.

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Section: ) Artificial Intelligence/ Machine Learning Methodsmentioning

confidence: 99%

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

2019

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“…It can be observed that increasing the input lag beyond one (combination (i)) generally did not improves the model accuracy. It is evident from the existing literature that increasing the input lag does not guarantee better forecast performance [93,94]. Sometimes, a high number of inputs has a negative impact on variance and causes a more complex model, leading to poor forecasting performance.…”

Section: Hourly Wind Power Prediction Using Nf-sc Nf-gp Lssvr and mentioning

confidence: 99%

Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation

et al. 2019

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Accurate predictions of wind speed and wind energy are essential in renewable energy planning and management. This study was carried out to test the accuracy of two different neuro fuzzy techniques (neuro fuzzy system with grid partition (NF-GP) and neuro fuzzy system with substractive clustering (NF-SC)), and two heuristic regression methods (least square support vector regression (LSSVR) and M5 regression tree (M5RT)) in the prediction of hourly wind speed and wind power using a cross-validation method. Fourfold cross-validation was employed by dividing the data into four equal subsets. LSSVR’s performance was superior to that of the M5RT, NF-SC, and NF-GP models for all datasets in wind speed prediction. The overall average root-mean-square errors (RMSE) of the M5RT, NF-GP, and NF-SC models decreased by 11.71%, 1.68%, and 2.94%, respectively, using the LSSVR model. The applicability of the four different models was also investigated in the prediction of one-hour-ahead wind power. The results showed that NF-GP’s performance was superior to that of LSSVR, NF-SC, and M5RT. The overall average RMSEs of LSSVR, NF-SC, and M5RT decreased by 5.52%, 1.30%, and 15.6%, respectively, using NF-GP.

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“…To address these issues, the variational mode decomposition (VMD) [32] algorithm is introduced in this study. Compared with EMD, WD, and other methods, VMD is an accurate mathematical model, which can decompose the original data into a set of variational mode functions (VMFs) that fluctuate around the center frequency [33][34][35], with better decomposition effect and higher robustness [36][37][38]. Currently, the contribution of VMD in the field of prediction has now been confirmed by some scholars, such as energy power generation prediction [39], carbon price prediction [40], runoff prediction [41], container throughput prediction [42] and air quality index prediction [43].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Decomposition—Ensemble Prediction Model for Dam Deformation

Cao

Bao

et al. 2020

Applied Sciences

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Accurate and reliable prediction of dam deformation (DD) is of great significance to the safe and stable operation of dams. In order to deal with the fluctuation characteristics in DD for more accurate prediction results, a new hybrid model based on a decomposition-ensemble model named VMD-SE-ER-PACF-ELM is proposed. First, the time series data are decomposed into subsequences with different frequencies and an error sequence (ER) by variational mode decomposition (VMD), and then the secondary decomposition method is introduced into the prediction of ER. In these two decomposition processes, the sample entropy (SE) method is innovatively utilized to determine the decomposition modulus. Then, the input variables of the subsequences are selected by partial autocorrelation analysis (PACF). Finally, the parameter-optimization-based extreme learning machine (ELM) models are used to predict the subsequences, and the outputs are reconstructed to obtain the final prediction results. The case analysis shows that the VMD-SE-ER-PACF-ELM model has strong prediction ability for DD. The model is then compared with other nonlinear and time series models, and its performance under different prediction periods is also analyzed. The results show that the proposed model is able to adequately describe the original DD. It performs well in both training and testing stages. It is a preferred data-driven model for DD prediction and can provide a priori knowledge for health monitoring of dams.

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A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine

Cited by 124 publications

References 35 publications

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation

A Novel Hybrid Decomposition—Ensemble Prediction Model for Dam Deformation

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