A novel hybrid wind speed interval prediction model based on mode decomposition and gated recursive neural network

Xu, Hao; Chang, Yuqing; Zhao, Yong; Wang, Fuli

doi:10.1007/s11356-022-21904-5

Cited by 7 publications

(2 citation statements)

References 35 publications

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“…Research on air quality index (AQI) time series forecasting based on mixed data decomposition and echo state network (Liu &Zhang 2021). A novel hybrid wind speed interval prediction model based on mode decomposition and gated recurrent neural network (Xu et al 2022). Dragomiretskiy proposed a signal decomposition method of VMD (variational modal decomposition) (Dragomiretskiy &Zosso 2014).…”

Section: Introductionmentioning

confidence: 99%

Monthly runoff prediction of the lower Yellow River based on the VMD-SSA-KELM model

Zhang,

Liu,

Ren

2023

Preprint

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Guiding the efficient utilization of water resources relies on a high-precision prediction of monthly runoff in the lower reaches of the Yellow River. disaster prevention and mitigation, water environmental protection, and ecological restoration. Based on the superior performance of VMD in processing non-stationary monthly runoff sequences, the multimodal optimization ability of SSA in the direction of data sequences, and the advantageous features of KELM model KELM model efficiency, tuning free, and memory friendliness, a monthly runoff prediction model for the lower Yellow River was established using the VMD-SSA-KELM coupling method, and apply the coupled model to predict the monthly runoff at Lijin Hydrological Station in Downstream Areas. The results indicate that the model has a reasonable predictive effect on the monthly runoff data of this hydrological station; it has high accuracy compared with the traditional prediction model, and the R2 of the prediction model for Lijin Hydrological Station reaches 0.97, with an average absolute error of 8.02, an average absolute percentage error of 0.44, and a root mean square error of 37.25; at the same time, the model can effectively extract the inherent feature information of the corresponding time series, improving the prediction performance of runoff data, it can make the monthly runoff forecast more accurate.

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Section: Introductionmentioning

confidence: 99%

Monthly runoff prediction of the lower Yellow River based on the VMD-SSA-KELM model

Zhang,

Liu,

Ren

2023

Preprint

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“…Machine Learning (ML) techniques have been broadly introduced in power load forecasts (Meira et al, 2021;Sun et al, 2022) and time series forecasts (Khan et al, 2020;Hossain et al, 2021;Patel et al 2022;Belmahdi et al, 2022;Xu et al, 2022a), mainly engaging point forecasts, interval predictions and probability density forecasts (Ibrahim et al, 2020;Wen and Yuan, 2020;Zhang et al, 2021;Zhang and Chen, 2022;Zhang and Wang, 2022;Liu et al, 2022). Point forecasting, the most frequently used technique, can provide a point estimate of the future load for each forecast horizon.…”

Section: Introductionmentioning

confidence: 99%

Deep learning–based neural networks for day-ahead power load probability density forecasting

Zhou

Zhu

Chen

et al. 2022

Environ Sci Pollut Res

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Highlights•Machine learning promotes energy utilization efficiency for reducing GHG emissions •Hybrid MCQRNN with Dropconnect and Dropout for load probability density forecasting •D-MCQRNN conquers overfitting and alleviates the time-lag of multi-output forecasts •D-MCQRNN improves the accuracy and reliability of load probability density forecasts Manuscript Click here to access/download;Manuscript;Manuscript marked with changes.docx Click here to view linked References

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A novel combined wind speed forecasting system based on fuzzy granulation and multi-objective optimization

Yang,

Wang

2024

Journal of Renewable and Sustainable Energy

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With the increasing application of wind energy, reliable wind speed prediction has become imperative. However, prior studies predominantly concentrated on single-model predictions, disregarding the inherent uncertainty in wind speed. This oversight resulted in inadequate deterministic and probabilistic forecasting outcomes across varying scenarios. To make up for these shortcomings, a novel forecasting system combining a data preprocessing technique, a sub-model selection method, and a modified multi-objective integrate optimization strategy is designed in this paper. According to the data obtained from China's wind farm, the forecasting efficiency of this system is verified from multiple perspectives. The findings show that the system takes advantage of each model to boost the precision and stability of point prediction successfully. Furthermore, it achieves higher interval coverage and narrower interval width under distinct confidence levels. These results highlight the system's potential as a reliable technical support for efficient dispatching of the entire power system.

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A novel hybrid wind speed interval prediction model based on mode decomposition and gated recursive neural network

Cited by 7 publications

References 35 publications

Monthly runoff prediction of the lower Yellow River based on the VMD-SSA-KELM model

Monthly runoff prediction of the lower Yellow River based on the VMD-SSA-KELM model

Deep learning–based neural networks for day-ahead power load probability density forecasting

A novel combined wind speed forecasting system based on fuzzy granulation and multi-objective optimization

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