摘 要
随着智能电网的发展，准确的负荷预测对于电力系统的稳定运行至关重要。本研究旨在构建基于人工智能的电网负荷预测模型，以提高预测精度和可靠性。针对传统方法在处理复杂非线性关系方面的局限性，提出了一种融合深度学习与时间序列分析的混合模型架构。该模型采用长短期记忆网络（LSTM）捕捉时序特征，并结合卷积神经网络（CNN）提取空间特征，同时引入注意力机制增强关键信息表达能力。通过对中国某地区实际电网数据进行实验验证，结果表明所提模型在不同时间尺度下的预测误差均低于3%，相比传统方法平均降低约20%。此外，模型具备良好的泛化性能，在多种工况下均能保持较高预测精度。本研究不仅为电网调度提供了有效的决策支持工具，也为后续研究奠定了理论基础，特别是在应对极端天气等特殊场景方面展现了独特优势。
关键词：负荷预测；深度学习；长短期记忆网络
Abstract
With the development of smart grids, accurate load forecasting has become crucial for the stable operation of power systems. This study aims to construct an artificial intelligence-based load forecasting model for electric grids to improve prediction accuracy and reliability. Addressing the limitations of traditional methods in handling complex nonlinear relationships, a hybrid model architecture that integrates deep learning with time series analysis is proposed. The model employs Long Short-Term Memory (LSTM) networks to capture temporal features and combines Convolutional Neural Networks (CNN) to extract spatial features, while incorporating an attention mechanism to enhance the representation of key information. Experimental validation using actual grid data from a region in China shows that the proposed model achieves prediction errors below 3% across different time scales, reducing errors by approximately 20% on average compared to traditional methods. Moreover, the model demonstrates excellent generalization performance, maintaining high prediction accuracy under various operating conditions. This research not only provides an effective decision-support tool for grid dispatch but also lays a theoretical foundation for future studies, particularly in addressing special scenarios such as extreme weather events.
Keywords: Load Forecasting；Deep Learning；Long Short-Term Memory Network
目 录
引言 1
一、电网负荷预测需求分析 1
（一）电力系统运行特点 1
（二）负荷预测的重要性 2
（三）现有方法的局限性 2
二、人工智能技术选型 2
（一）常用算法综述 3
（二）模型选择依据 3
（三）技术可行性分析 4
三、预测模型构建与优化 4
（一）数据预处理方法 4
（二）模型架构设计 4
（三）参数调优策略 5
四、模型验证与应用评估 5
（一）测试数据集构建 5
（二）验证指标体系 6
（三）实际应用效果 6
结 论 7
致 谢 8
参考文献 9
随着智能电网的发展，准确的负荷预测对于电力系统的稳定运行至关重要。本研究旨在构建基于人工智能的电网负荷预测模型，以提高预测精度和可靠性。针对传统方法在处理复杂非线性关系方面的局限性，提出了一种融合深度学习与时间序列分析的混合模型架构。该模型采用长短期记忆网络（LSTM）捕捉时序特征，并结合卷积神经网络（CNN）提取空间特征，同时引入注意力机制增强关键信息表达能力。通过对中国某地区实际电网数据进行实验验证，结果表明所提模型在不同时间尺度下的预测误差均低于3%，相比传统方法平均降低约20%。此外，模型具备良好的泛化性能，在多种工况下均能保持较高预测精度。本研究不仅为电网调度提供了有效的决策支持工具，也为后续研究奠定了理论基础，特别是在应对极端天气等特殊场景方面展现了独特优势。
关键词：负荷预测；深度学习；长短期记忆网络
Abstract
With the development of smart grids, accurate load forecasting has become crucial for the stable operation of power systems. This study aims to construct an artificial intelligence-based load forecasting model for electric grids to improve prediction accuracy and reliability. Addressing the limitations of traditional methods in handling complex nonlinear relationships, a hybrid model architecture that integrates deep learning with time series analysis is proposed. The model employs Long Short-Term Memory (LSTM) networks to capture temporal features and combines Convolutional Neural Networks (CNN) to extract spatial features, while incorporating an attention mechanism to enhance the representation of key information. Experimental validation using actual grid data from a region in China shows that the proposed model achieves prediction errors below 3% across different time scales, reducing errors by approximately 20% on average compared to traditional methods. Moreover, the model demonstrates excellent generalization performance, maintaining high prediction accuracy under various operating conditions. This research not only provides an effective decision-support tool for grid dispatch but also lays a theoretical foundation for future studies, particularly in addressing special scenarios such as extreme weather events.
Keywords: Load Forecasting；Deep Learning；Long Short-Term Memory Network
目 录
引言 1
一、电网负荷预测需求分析 1
（一）电力系统运行特点 1
（二）负荷预测的重要性 2
（三）现有方法的局限性 2
二、人工智能技术选型 2
（一）常用算法综述 3
（二）模型选择依据 3
（三）技术可行性分析 4
三、预测模型构建与优化 4
（一）数据预处理方法 4
（二）模型架构设计 4
（三）参数调优策略 5
四、模型验证与应用评估 5
（一）测试数据集构建 5
（二）验证指标体系 6
（三）实际应用效果 6
结 论 7
致 谢 8
参考文献 9
