State transition ANNs for hourly wind speed forecasting

Yuan, Deyu; Zheng, Qian; Pei, Yan

doi:10.1109/cac.2017.8244027

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…The prediction method based on statistical theory has been widely used recently. Common statistical models include grey model (GM) [18], [19], autoregressive integrated moving average (ARIMA) [20], [21], support vector regression (SVR) [22], [23], multiple linear regression (MLR) [24], long-term and short-term memory network (LSTM) [9], [25], [26], artificial neural network(ANN) [27], machine learning algorithm(ML) [22], deep learning algorithm(DL) [29] and other hybrid models [30]. More and more researchers use machine learning methods to extract internal patterns from data.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Prediction Method for Wind Speed Combining Ensemble Empirical Mode Decomposition and Bayesian Ridge Regression

Yang

2020

IEEE Access

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In recent years, with the rapid development of wind power generation, some problems are gradually highlighted. At present, one of the essential methods to solve these problems is to predict wind speed. In this paper, a hybrid BRR-EEMD method is proposed for short-term wind speed prediction based on the Bayesian ridge regression prediction method and ensemble empirical mode decomposition. We use ensemble empirical mode decomposition of the hybrid method to decompose complex time series of wind speed into several relatively milder, more regular, and stable subsequences. Then each subsequence is carried out by using the Bayesian ridge regression method. The value of each subsequence is predicted by it. Finally, the value of multiple subsequences is fused to form the prediction results of the original complex time series of wind speed. In order to verify the proposed method comprehensively, this paper selects two data to test. According to the results, predicted values have shown higher accuracy compared with the various prediction methods. Therefore, the hybrid BRR-EEMD method is accurate and effective in predicting wind speed, which has practical significance and potential value. INDEX TERMS Ensemble empirical mode decomposition, short-term predicting, Bayesian ridge regression, wind speed, time series.

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Section: Introductionmentioning

confidence: 99%

Hybrid Prediction Method for Wind Speed Combining Ensemble Empirical Mode Decomposition and Bayesian Ridge Regression

Yang

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

State transition ANNs for hourly wind speed forecasting

Cited by 1 publication

References 16 publications

Hybrid Prediction Method for Wind Speed Combining Ensemble Empirical Mode Decomposition and Bayesian Ridge Regression

Hybrid Prediction Method for Wind Speed Combining Ensemble Empirical Mode Decomposition and Bayesian Ridge Regression

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