Ultra-short-term wind power interval prediction based on multi-task learning and generative critic networks

Shi, Jinhao; Wang, Bo; Luo, Kaiyi; Wu, Yifei; Zhou, Min; Watada, Junzo

doi:10.1016/j.energy.2023.127116

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…To verify the impact of the GA-BP network model on the short-term wind power modeling ability, the measured wind power of a wind farm in the Netherlands was analyzed for the whole year of 2021, and the actual wind power was predicted for the 24h period from March15 to March 30, ignoring environmental factors. In short, 2928 parameters were used as training samples, 360 parameters were used as testing samples, and 1h was set as the sampling time point to fit and test the model based on the data set above [11].…”

Section: Example Simulation Analysismentioning

confidence: 99%

Construction and simulation study of wind power prediction model based on BP neural network

2023

Second International Conference on Sustainable Technology and Management (ICSTM 2023)

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Along with the continuous urbanization process, the level of electricity consumption in China's urban and rural areas has been growing, which also generates huge pressure for the electricity load. And the introduction of wind power model provides corresponding support to relieve the pressure of electricity consumption. The use of wind power prediction model can ensure the scientific regulation of power and realize the stable transmission of power system. There are more ways for wind power prediction, and the application of wind power prediction model based on neural network can improve the use of efficiency. Therefore, the depth of neural network should be deepened. Based on the above analysis, this paper discusses the wind power prediction model based on BP neural network, and at the same time, simulation experiments are implemented. The results confirm that this model can improve the accuracy of wind power prediction, while reducing the required sample size of the neural network, enhancing its generalization ability and ensuring the stable operation of wind power.

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Section: Example Simulation Analysismentioning

confidence: 99%

Construction and simulation study of wind power prediction model based on BP neural network

2023

Second International Conference on Sustainable Technology and Management (ICSTM 2023)

View full text Add to dashboard Cite

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A novel ultra-short-term wind speed prediction method based on dynamic adaptive continued fraction

Jin,

Tian,

et al. 2024

Chaos, Solitons & Fractals

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High and low frequency wind power prediction based on Transformer and BiGRU-Attention

Wang,

Shi,

Yang

et al. 2024

Energy

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Ultra-short-term wind power interval prediction based on multi-task learning and generative critic networks

Cited by 5 publications

References 34 publications

Construction and simulation study of wind power prediction model based on BP neural network

Construction and simulation study of wind power prediction model based on BP neural network

A novel ultra-short-term wind speed prediction method based on dynamic adaptive continued fraction

High and low frequency wind power prediction based on Transformer and BiGRU-Attention

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