Multi-device wind turbine power generation forecasting based on hidden feature embedding

• Published in: Frontiers in energy research 2024-07, Vol.12
• Main Authors: Man, Junfeng, Xu, Ke, Wang, Dian, Liu, Yong, Zhan, Jun, Qiu, Yongfeng
• Format: Article
• Language: English
• Subjects: hidden feature; long short-term memory; space embedding; time series forecasting; wind power generation
• ISSN: 2296-598X; 2296-598X
• DOI: 10.3389/fenrg.2024.1346369

In recent years, the global installed capacity of wind power has grown rapidly. Wind power forecasting, as a key technology in wind turbine systems, has received widespread attention and extensive research. However, existing studies typically focus on the power prediction of individual devices. In the context of multi-turbine scenarios, employing individual models for each device may introduce challenges, encompassing data dilution and a substantial number of model parameters in power generation forecasting tasks. In this paper, a single-model method suitable for multi-device wind power forecasting is proposed. Firstly, this method allocates multi-dimensional random vectors to each device. Then, it utilizes space embedding techniques to iteratively evolve the random vectors into representative vectors corresponding to each device. Finally, the temporal features are concatenated with the corresponding representative vectors and inputted into the model, enabling the single model to accomplish multi-device wind power forecasting task based on device discrimination. Experimental results demonstrate that our method not only solves the data dilution issue and significantly reduces the number of model parameters but also maintains better predictive performance. Future research could focus on using more interpretable space embedding techniques to observe representation vectors of wind turbine equipment and further explore their semantic features.