Novel wind-speed prediction system based on dimensionality reduction and nonlinear weighting strategy for point-interval prediction

In the context of today’s energy shortage, wind energy plays a crucial role as one of the most widely used renewable energy sources. However, in order to fully utilize the potential of wind energy, it is necessary to build a precise and reliable wind-speed prediction system. Conventional wind-speed...

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Vydáno v:Expert systems with applications Ročník 241; s. 122477
Hlavní autoři: Wang, Xinyu, Wang, Jianzhou, Niu, Xinsong, Wu, Chunying
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.05.2024
Témata:
Combined model
Dimensionality reduction
Multi-objective optimization algorithm
Neural networks
Wind-speed prediction
Dimensionality reduction
Combined model
Multi-objective optimization algorithm
Neural networks
Wind-speed prediction
ISSN:0957-4174, 1873-6793
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Shrnutí:In the context of today’s energy shortage, wind energy plays a crucial role as one of the most widely used renewable energy sources. However, in order to fully utilize the potential of wind energy, it is necessary to build a precise and reliable wind-speed prediction system. Conventional wind-speed prediction models frequently rely on a solitary model for forecasting, and sometimes these models cannot accurately fit the nonlinear characteristics of the data, resulting in poor prediction performance. Therefore, to address this deficiency, a new wind-speed point-interval prediction system is proposed in this study. The fuzzy information granulation technology is adopted in this system to reduce the dimension of data and solve the problem of redundancy in wind-speed data. In addition, this paper uses the multi-objective dragonfly algorithm and combines sub-models using the multi-nonlinear weight strategy to address the issue of inadequate precision exhibited by an individual prediction model. In order to verify the effectiveness of this wind-speed prediction system, the Penglai wind farm in Shandong Province is used as the dataset to construct the model, and several reference models are selected to conduct point prediction and interval prediction comparison experiments respectively. The experimental results show that the combined prediction system has the best prediction performance, with MAPE values of 14.16%, 15.02% and 15.74%, respectively. Compared with other benchmark models, the average improvement rates for the multi-step prediction were 14.97%, 16.48% and 19.11%, respectively.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.122477