PDF(Pubmed)
Abstract：
The traditional method for power load forecasting is susceptible to various factors, including holidays, seasonal variations, weather conditions, and more. These factors make it challenging to ensure the accuracy of forecasting results. Additionally, there is a limitation in extracting meaningful physical signs from power data, which ultimately reduces prediction accuracy. This paper aims to address these issues by introducing a novel approach called VCAG (Variable Mode Decomposition-Convolutional Neural Network-Attention Mechanism-Gated Recurrent Unit) for combined power load forecasting. In this approach, we integrate Variable Mode Decomposition (VMD) with Convolutional Neural Network (CNN). VMD is employed to decompose power load data, extracting valuable time-frequency features from each component. These features then serve as input for the CNN. Subsequently, an attention mechanism is applied to give importance to specific features generated by the CNN, enhancing the weight of crucial information. Finally, the weighted features are fed into a Gated Recurrent Unit (GRU) network for time series modeling, ultimately yielding accurate load forecasting results.To validate the effectiveness of our proposed model, we conducted experiments using two publicly available datasets. The results of these experiments demonstrate that our VCAG method achieves high accuracy and stability in power load forecasting, effectively overcoming the limitations associated with traditional forecasting techniques. As a result, this approach holds significant promise for broad applications in the field of power load forecasting.
摘要：
传统的电力负荷预测方法易受各种因素的影响,包括节假日,季节性变化,天气条件,还有更多.这些因素使得确保预测结果的准确性具有挑战性。此外,从电力数据中提取有意义的体征是有限制的，这最终降低了预测的准确性。本文旨在通过引入一种称为VCAG（可变模式分解-卷积神经网络-注意力机制-控制的递归单元）的组合电力负荷预测新方法来解决这些问题。在这种方法中，我们将变模分解(VMD)与卷积神经网络(CNN)集成。VMD用于分解电力负荷数据，从每个分量中提取有价值的时频特征。这些特征然后用作CNN的输入。随后，注意机制被应用于对CNN生成的特定特征给予重视，增强关键信息的权重。最后,加权特征被馈送到门控递归单元(GRU)网络中进行时间序列建模，最终产生准确的负荷预测结果。为了验证我们提出的模型的有效性，我们使用两个公开的数据集进行了实验.这些实验结果表明，我们的VCAG方法在电力负荷预测中具有很高的准确性和稳定性。有效地克服了传统预测技术的局限性。因此,这种方法在电力负荷预测领域具有广泛的应用前景。
