A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Ultra-short-term multi-step wind power prediction based on fractal scaling factor transformation
Most of the existing ultra-short-term wind power prediction methods only involve mathematical models and rarely consider spatial correlation factors. As such, the prediction system remains to be further improved. In this paper, in order to accurately predict the wind power of the large-scale wind farm, the idea of spatial-temporal scale transformation is introduced to establish a spatial up-scaling model of ultra-short-term multi-step wind power prediction based on fractal scaling factor transformation. First, the regional division of the large-scale wind farm is carried out. Then, the affine relationship of the local and whole regions is established by using the theory of stretching transformation of fractal. The process of traditional space up-scaling prediction is improved by fractal transformation. Finally, the deductive process is accomplished by the prediction of local regional to whole regional. To verify the proposed approach, two large-scale wind farms in northeast China were selected to predict its wind power. Compared with the traditional wind power prediction approach of the whole wind farm and the prediction approach of traditional spatial up-scaling, this approach can get better prediction accuracy.
Ultra-short-term multi-step wind power prediction based on fractal scaling factor transformation
Most of the existing ultra-short-term wind power prediction methods only involve mathematical models and rarely consider spatial correlation factors. As such, the prediction system remains to be further improved. In this paper, in order to accurately predict the wind power of the large-scale wind farm, the idea of spatial-temporal scale transformation is introduced to establish a spatial up-scaling model of ultra-short-term multi-step wind power prediction based on fractal scaling factor transformation. First, the regional division of the large-scale wind farm is carried out. Then, the affine relationship of the local and whole regions is established by using the theory of stretching transformation of fractal. The process of traditional space up-scaling prediction is improved by fractal transformation. Finally, the deductive process is accomplished by the prediction of local regional to whole regional. To verify the proposed approach, two large-scale wind farms in northeast China were selected to predict its wind power. Compared with the traditional wind power prediction approach of the whole wind farm and the prediction approach of traditional spatial up-scaling, this approach can get better prediction accuracy.
Ultra-short-term multi-step wind power prediction based on fractal scaling factor transformation
Yang, Mao (author) / Chen, Xinxin (author) / Huang, Binyang (author)
2018-09-01
17 pages
Article (Journal)
Electronic Resource
English
Prediction of ultra-short-term wind power based on BBO-KELM method
| American Institute of Physics | 2019
Short-Term Multi-Step Wind Direction Prediction Based on OVMD Quadratic Decomposition and LSTM
| DOAJ | 2023
A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning
| BASE | 2020
Ultra-short-term wind speed prediction based on deep spatial-temporal residual network
| American Institute of Physics | 2023