Ultra-Short Term Prediction of Wind Power Based on Multiples Model Extreme Leaning Machine

Huang, Ting; Wang, Xin; Li, Lixue; Zhou, Lei; Yao, Gang

doi:10.1007/978-3-642-21111-9_61

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…In 2011, Huang and collaborators performed ultra-short wind power forecasts based on the Multiple Models Extreme Learning Machine (MMELM), where the final output is given by the weighted sum of all the model outputs, and they succeeded in obtaining accurate results [215].…”

Section: Nonlinear and Hybrid Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Very Short-Term Wind Power Forecasting: State-of-the-Art

Mendes¹,

Sumaili²,

Bessa³

et al. 2014

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Section: Nonlinear and Hybrid Methodsmentioning

confidence: 99%

“…[214], [215] Grey theory has the ability to deal with systems characterized by poor or nonexistent information. When applied to wind forecast, grey predictors deliver improved wind speed prediction results over persistence, particularly for low-rated time series.…”

Section: Grey Predictor [200]-[210]mentioning

confidence: 99%

Very Short-Term Wind Power Forecasting: State-of-the-Art

Mendes¹,

Sumaili²,

Bessa³

et al. 2014

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“…The prediction model only needed to determine the appropriate number of hidden layer nodes, the input layer weights and the hidden layer biases can be initialised randomly, and the network output weights were calculated by the generalised inverse matrix. This method for selecting network parameters used by ELM gave it a substantial increase in generalisation ability and learning speed compared to traditional neural networks 21 . However, despite these attractive features, ELM solutions also had some drawbacks.…”

Section: Introductionmentioning

confidence: 99%

“…This method for selecting network parameters used by ELM gave it a substantial increase in generalisation ability and learning speed compared to traditional neural networks. 21 However, despite these attractive features, ELM solutions also had some drawbacks. ELMs based on the empirical risk minimisation (ERM) principle 22 tended to lead to the model overfitting.…”

mentioning

confidence: 99%

An effective wind speed prediction model combining secondary decomposition and regularised extreme learning machine optimised by cuckoo search algorithm

Zhang

Guo

et al. 2022

Wind Energy

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Wind speed prediction has an important impact on the planning, economic operation and safe maintenance of wind power systems. However, the high volatility and intermittency of wind speed make it difficult to predict accurately. To improve the prediction accuracy, we developed a hybrid multistep wind speed prediction model named EWP-CS-RELM. In this model, a secondary decomposition technique of ensemble empirical mode decomposition (EEMD) and wavelet packet transform (WPT) is used, and it is called the EWP decomposition technique. This decomposition technique can achieve an adaptive processing of the data and accurately extract the characteristic components of the signal, avoiding the occurrence of pattern overlap and reducing the mutual interference between components. At the same time, the high and low-frequency parts of the complex signal (component) can be decomposed into different frequency bands, and the corresponding frequency band can be selected adaptively to match the signal spectrum. The subsequence obtained after EWP decomposition is then predicted using a regularised extreme learning machine (RELM) optimised by the cuckoo search (CS) algorithm with strong global search ability to obtain the results. The hybrid prediction model is validated using four seasons of wind speed data from two wind farms in Shandong, China, and compared with seven other prediction models. Simulation results illustrate that the EWP-CS-RELM model outperforms the other seven models with the smallest statistical errors.

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Short-Term Wind Power Prediction Based on Wavelet Decomposition and Extreme Learning Machine

Wang

Zheng

et al. 2012

Lecture Notes in Computer Science

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Ultra-Short Term Prediction of Wind Power Based on Multiples Model Extreme Leaning Machine

Cited by 3 publications

References 8 publications

Very Short-Term Wind Power Forecasting: State-of-the-Art

Very Short-Term Wind Power Forecasting: State-of-the-Art

An effective wind speed prediction model combining secondary decomposition and regularised extreme learning machine optimised by cuckoo search algorithm

Short-Term Wind Power Prediction Based on Wavelet Decomposition and Extreme Learning Machine

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