Large-scale ensemble learning for robust wind power forecasting with supercomputing

Yujiang Long; Wei Wei; Ye Zhong

doi:10.1117/12.2681713

23 May 2023 Large-scale ensemble learning for robust wind power forecasting with supercomputing

Yujiang Long, Wei Wei, Ye Zhong

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126452G (2023) https://doi.org/10.1117/12.2681713
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Wind power is renewable energy and plays a critical role in the carbon emission peak and carbon neutrality plan. As wind speed and direction is unstable, the generated electricity power fluctuates, and therefore cannot directly accepted by the power grid. A common solution is to forecast the wind power generation and adjust the power generation and dispatching strategy to improve the stability and acceptance when provided to the power grid based on the forecasting results. The accurate forecasting of wind power is the key to this scheme. Common forecasting methods include physical approach and statistical approach. For physical approach, the Numerical Weather Prediction (NWP) model forecasts the wind speed and direction, so that the power is directly calculated. For statistical approach, the wind power is forecasted according to historical time series and predicted weather. The forecasting model including statistical autoregressive models, support vector machines, and neural networks, etc. However, the single model prediction lacks robustness, so we propose to ensemble multiple models to achieve robust short-term wind power forecasting. Based on supercomputing, we propose a large-scale parallel forecasting of various methods and models and a statistical method to assign reasonable weights to various models, achieving the large-scale ensemble of various models. In order to adapt to real-time data distribution changes, we update the models and corresponding weights online. The experiments are conducted on a dataset of wind power generation and meteorology from a province in China. The experimental results show that compared with the single model, the proposed large-scale ensemble model achieves better forecast accuracy and robustness.

Citation Download Citation

Yujiang Long, Wei Wei, and Ye Zhong "Large-scale ensemble learning for robust wind power forecasting with supercomputing", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126452G (23 May 2023); https://doi.org/10.1117/12.2681713

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KEYWORDS

Wind energy

Data modeling

Atmospheric modeling

Statistical modeling

Autoregressive models

Online learning

Power grids

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