利用VMD-ELM模型集合对爱尔兰的风力发电进行超前多步预测

J. M. González-Sopeña, V. Pakrashi, Bidisha Ghosh
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引用次数: 5

摘要

准确的风电预测是电网和能源交易市场正确运行的关键工具,特别是在风力资源丰富的地区,如爱尔兰,风能占发电量的很大一部分。本文提出了一种基于变分模态分解和极限学习机的多步超前风电预测集成模型,并将其应用于爱尔兰风电场。来自位于不同地点的两个风力发电场的数据被用来证明该模型对爱尔兰的适用性。结果表明,使用这种完整的模型集合可以为多个预测层提供更可靠和稳健的预测,并且与单一模型相比提高了7%至22%。此外,无论预测水平如何,该集合都具有较低的系统误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-step ahead wind power forecasting for Ireland using an ensemble of VMD-ELM models
Accurate wind power forecasts are a key tool for the correct operation of the grid and the energy trading market, particularly in regions with a large wind resource as Ireland, where wind energy comprises a large share of the electricity generated. A multi-step ahead wind power forecasting ensemble of models based on variational mode decomposition and extreme learning machines is employed in this paper to be applied for Irish wind farms. Data from two wind farms placed in different locations are used to show the suitability of the model for Ireland. The results show that the use of this full ensemble of models provides more reliable and robust forecasts for several prediction horizons and an improvement between 7% and 22% with respect to a single model. Additionally, the ensemble shows a low systematic error regardless of the prediction horizon.
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