Short-term wind speed forecasting system based on multivariate time series and multi-objective optimization

Wind energy has attracted much attention because it is sustainable and renewable energy with a much smaller impact on the environment than fossil fuels. Accurate wind speed forecasting can pave the way to the goal of sustainable development. However, many papers only use wind speed series but ignore weather factors, which lead to poor forecasting results. In this paper, we propose an ensemble system that considers historical wind speed series and other weather factors to make wind speed forecasting. The proposed system integrates noise reduction, clustering, and multi-objective optimization. Firstly, the historical wind speed series are decomposed by complementary ensemble empirical mode decomposition (CEEMD). Secondly, the samples are clustered by self-organizing map (SOM) according to the weather factors. Finally, a regularized extreme learning machine (RELM) model is trained to forecast the wind speed for each cluster, and multi-objective grey wolf optimizer (MOGWO) is employed to optimize the parameters in the model. An empirical study using three datasets from the M2 tower of the national wind power technology center (NWTC) of the national renewable energy laboratory (NREL) illustrates that the proposed system performs better than other comparative models in terms of four performance indicators. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Wind energy is gaining much attention as a sustainable and renewable energy source with lower environmental impact compared to fossil fuels. Accurate wind speed forecasting is crucial for sustainable development. This study proposes an ensemble system that takes into account historical wind speed series and other weather factors, outperforming other comparative models in terms of forecasting performance.