基于CNN-SAEDN-Res的短期电力负荷预测方法
Short-term power load forecasting method based on CNN-SAEDN-Res
AI总结：
针对含非时间因素的负荷数据导致序列模型预测精度不足的问题，提出结合CNN特征提取、SAEDN初始预测与残差修正的短期负荷预测方法，在预测精度和稳定性上更具优势。
摘要：
在深度学习领域，包含非时间因素的负荷数据难以被序列模型处理，该问题会导致预测精度不足。为此，本文提出一种基于卷积神经网络（convolutional neural network, CNN）、自注意力编解码网络（self-attention encoder-decoder network, SAEDN）和残差修正（residual-refinement, Res）的短期负荷预测方法。该方法中，特征提取模块由二维卷积神经网络构成，用于挖掘数据间的局部相关性，获取高维数据特征。初始负荷预测模块由自注意力编解码网络和前馈神经网络（feedforward neural network, FFN）组成，该模块利用自注意力机制对高维特征进行编码，可获取数据间的全局相关性，使模型在混杂非时间序列因素的数据中，能依据数据间的耦合关系保留重要信息；随后进行自注意力解码，并通过前馈神经网络回归得到初始负荷。本文引入残差机制构建负荷优化模块，该模块生成残差负荷值以优化初始负荷。仿真结果表明，所提负荷预测方法在预测精度和预测稳定性方面均具有优势。
英文摘要：
In deep learning, the load data with non-temporal factors are difficult to process by sequence models. This problem results in insufficient precision of the prediction. Therefore, a short-term load forecasting method based on convolutional neural network (CNN), self-attention encoder-decoder network (SAEDN) and residual-refinement (Res) is proposed. In this method, feature extraction module is composed of a two-dimensional convolutional neural network, which is used to mine the local correlation between data and obtain high-dimensional data features. The initial load fore-casting module consists of a self-attention encoder-decoder network and a feedforward neural network (FFN). The module utilizes self-attention mechanisms to encode high-dimensional features. This operation can obtain the global correlation between data. Therefore, the model is able to retain important information based on the coupling relationship between the data in data mixed with non-time series factors. Then, self-attention decoding is per-formed and the feedforward neural network is used to regression initial load. This paper introduces the residual mechanism to build the load optimization module. The module generates residual load values to optimize the initial load. The simulation results show that the proposed load forecasting method has advantages in terms of prediction accuracy and prediction stability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
