基于渐进式分解架构的风电时间序列预测
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国家自然科学基金面上项目(62076045); 辽宁省教育厅揭榜挂帅服务地方项目(LJKFZ20220290); 大连大学学科交叉项目(DLUXK-2023-YB-003, DLUXK-2023-YB-009, DLUXK-2022-ZD-003); 高等学校学科创新引智计划(D23006)


Time Series Forecasting of Wind Power Based on Progressive Decomposition Architecture
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    摘要:

    准确预测风电机组各项指标对准确管控机组和调控电网的供需有着重要意义. 预测指标任务可抽象为风电时间序列预测任务. 目前时间序列预测模型主要采用深度学习模型, 但是风电时间序列具有较强的波动性和随机性, 导致绝大部分模型不能较好挖掘风电时间序列的复杂演化特性. 为解决上述问题, 提出了一种基于渐进式分解架构的风电时间序列预测方法, 该方法首先应用神经网络池化分解方法将复杂的依赖关系简化并应用注意力机制学习长期趋势, 然后运用多变量融合捕捉模块增强了网络整体的多变量关联挖掘能力, 最后, 融合趋势项和周期项对风电时间序列做出准确的预测. 实验结果表明, 该方法在风电时间序列的多步预测中均方误差相比基线模型至高可提升24%, 在多尺度预测长度下表现出预测性能稳定提升的同时, 计算效率显著优于同类模型.

    Abstract:

    Accurate prediction of wind turbine metrics is important for accurate control of turbines and the regulation of grid supply and demand. The task of forecasting these indicators can be abstracted as a task of wind power time series forecasting. Currently, deep learning models are mainly used in time series prediction models, but the strong volatility and randomness of wind power time series often prevent most models from effectively capturing the complex evolutionary characteristics of the data. To address these issues, a wind power time series forecasting method based on a progressive decomposition architecture is proposed, which first applies a neural network pooling decomposition method to simplify complex dependencies and then applies an attention mechanism to learn long-term trends. Subsequently, a multivariate fusion capture module is employed to enhance the overall multivariate correlation mining ability of the network, and it fuses the trend term and the period term to make accurate forecasts of the wind power time series. Finally, the trend and period terms are fused to make accurate forecasts for wind power time series. Experimental results show that this method can achieve up to a 24% reduction in mean squared error (MSE) for wind power time series forecasting compared to baseline models. It also exhibits stable improvements in predictive performance across multiple forecasting lengths while significantly outperforming similar models in computational efficiency.

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丁浩,周成杰,车超,赵天明,周守亮.基于渐进式分解架构的风电时间序列预测.计算机系统应用,,():1-9

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  • 收稿日期:2024-01-15
  • 最后修改日期:2024-02-26
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  • 在线发布日期: 2024-05-31
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