Multi-Step Short-Term Wind Power Prediction Based on Three-level Decomposition and Improved Grey Wolf Optimization

Han, Yi; Tong, Xiangqian

doi:10.1109/access.2020.2984851

Cited by 18 publications

(10 citation statements)

References 45 publications

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“…When |B| > 1, the wolves move independently to search for their prey. When |B| < 1, wolves would act collectively to capture the searched prey [19]. At the same time, the whole process would make the selected wolf always have the best quality searchability.…”

Section: Improved Grey Wolf Optimization Supportmentioning

confidence: 99%

Impact of Participatory Community Planning on Publicity in Public Space Renewal Based on Machine Learning Algorithm Based on Child-friendly Concept

Yang

2022

International Transactions on Electrical Energy Systems

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In the existing community planning and implementation, the design concept often does not take into account the needs of children, but only blindly pursues standardization and modernization. This would make the entire community structure unfriendly to children. Factors such as insufficient public play areas, messy traffic network design, and lack of recreational facilities suitable for children would limit children’s fun in the community’s public spaces. Children’s happy growth would be greatly hindered. In order to solve the above problems, this paper has put forward the child-friendly concept and has applied it to community planning, so as to carry out scientific research on the original participatory community planning model based on the support vector machine technology under the machine learning algorithm. And the improved gray wolf optimization algorithm is combined with it for the shortcomings of the technology’s algorithm accuracy and other performance, which is not good enough. The final optimization algorithm has played an extremely important role in testing the impact of the participatory community planning model on publicity in the renewal of public spaces. The experimental results have shown that the highest accuracy of the improved gray wolf optimization support vector machine technology can reach 97.88% and the highest Kappa coefficient can reach 96.83%, which greatly improves the feasibility of the research on the impact of public space publicity.

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Section: Improved Grey Wolf Optimization Supportmentioning

confidence: 99%

Impact of Participatory Community Planning on Publicity in Public Space Renewal Based on Machine Learning Algorithm Based on Child-friendly Concept

Yang

2022

International Transactions on Electrical Energy Systems

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“… CEEMDAN-Multi-objective GWO-KNEA 231 Parameters of KNEA. KELM-Improved GWO (IGWO) 232 Parameters of KELM. Improved multi-objective GWO 233 Parameter of ANN models (SVM, GRNN, BPNN, ANFIS, LSTM, and NARNN).…”

Section: State-of-the-art Deterministic Forecasting Methodsmentioning

confidence: 99%

An overview of deterministic and probabilistic forecasting methods of wind energy

Xie

et al. 2023

iScience

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“…Liu et al used the WPD-EMD-ELM model for wind speed prediction and the results showed that the model outperformed other hybrid models (Liu et al 2018a ). Han and Tong decomposed the data first using WPD-VMD-SSA and then predicted short-term wind power by KELM, while adding an Improved Grey Wolf Optimization (IGWO) to the model, and the results showed that the final model outperformed other comparative models (Han and Tong 2020 ) and so on.…”

Section: Introductionmentioning

confidence: 99%

Predicting the monthly consumption and production of natural gas in the USA by using a new hybrid forecasting model based on two-layer decomposition

Jiang

Zhao

2023

Environ Sci Pollut Res

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As an efficient, economical, and clean energy, natural gas plays an important role in the development of the new energy revolution. Accurate prediction of natural gas consumption and production can adjust energy deployment in advance, which can ensure the stable operation of natural gas. Considering the complex and non-linear characteristics of natural gas production and consumption data, this paper develops a new hybrid forecasting model (WPD-VMD-LSTM) based on the fuzzy entropy, variational mode decomposition (VMD), wavelet packet decomposition (WPD), and Long Short-Term Memory (LSTM). In this model, WPD and VMD undertake the tasks of primary and secondary decompositions, respectively; fuzzy entropy is used for the preprocessing process before the re-decomposition; and LSTM is used to predict the decomposed time series. In particular, the different criteria set by fuzzy entropy lead to the establishment of two prediction models. Then, two models are used to study monthly natural gas consumption and production in the USA. The results demonstrate that the proposed model performs significantly better than other comparable models and the target model has some practical value. Meanwhile, models may cope with different types of energy data, and models can accurately predict energy transformations with strong applicability, which can be applied to future energy forecasting in various fields. Finally, the constructed models are used to forecast the NGC and NGP in the USA in the next 3 years and make reasonable policy recommendations based on the forecast results.

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Multi-Step Short-Term Wind Power Prediction Based on Three-level Decomposition and Improved Grey Wolf Optimization

Cited by 18 publications

References 45 publications

Impact of Participatory Community Planning on Publicity in Public Space Renewal Based on Machine Learning Algorithm Based on Child-friendly Concept

Impact of Participatory Community Planning on Publicity in Public Space Renewal Based on Machine Learning Algorithm Based on Child-friendly Concept

An overview of deterministic and probabilistic forecasting methods of wind energy

Predicting the monthly consumption and production of natural gas in the USA by using a new hybrid forecasting model based on two-layer decomposition

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