摘 要
电力负荷预测对于电力系统的稳定运行和优化调度至关重要，随着智能电网的发展以及可再生能源的接入，传统预测方法难以满足日益复杂的电力系统需求。为此，本文提出基于深度学习的电力负荷预测模型，旨在提高预测精度并增强模型适应性。首先构建包含长短期记忆网络（LSTM）与卷积神经网络（CNN）相结合的混合架构，利用CNN提取局部特征，LSTM捕捉时间序列依赖关系，同时引入注意力机制以突出重要特征。通过对比实验验证该模型在不同场景下的性能表现，结果表明所提模型相较于传统方法具有更高的预测精度，在处理非线性、多变的电力负荷数据方面展现出显著优势。此外，针对季节性和周期性因素的影响，采用分解重构策略对原始数据进行预处理，进一步提升预测效果。研究发现该模型能够有效应对电力负荷的波动特性，为电力系统规划、运行提供可靠依据，其创新之处在于融合多种深度学习技术，并结合电力负荷特点设计针对性算法，为后续研究提供了新的思路和方向。
关键词：电力负荷预测 深度学习 LSTM与CNN混合架构
Abstract
Electric load forecasting is critical for the stable operation and optimal scheduling of power systems. With the development of smart grids and the integration of renewable energy sources, traditional forecasting methods struggle to meet the increasingly complex demands of modern power systems. To address this challenge, this paper proposes a deep learning-based electric load forecasting model aimed at improving prediction accuracy and enhancing model adaptability. The proposed model integrates a hybrid architecture combining Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNNs), leveraging CNNs to extract local features and LSTM to capture time-series dependencies. Additionally, an attention mechanism is introduced to highlight significant features. Comparative experiments validate the model's performance across different scenarios, demonstrating superior prediction accuracy compared to traditional methods, particularly in handling nonlinear and volatile electric load data. Furthermore, to account for seasonal and periodic factors, a decomposition-reconstruction strategy is employed for preprocessing the raw data, further enhancing forecasting outcomes. Research findings indicate that the model effectively addresses the fluctuating characteristics of electric loads, providing a reliable basis for power system planning and operation. The innovation lies in integrating multiple deep learning techniques and designing targeted algorithms based on the specific characteristics of electric loads, offering new insights and directions for future research.
Keyword:Electric Load Forecasting Deep Learning Lstm And Cnn Hybrid Architecture
目 录
引言 1
1电力负荷预测研究背景与意义 1
1.1电力系统发展概述 1
1.2负荷预测的重要性 2
1.3深度学习的应用前景 2
2深度学习模型在电力负荷预测中的应用 3
2.1常用深度学习模型综述 3
2.2模型选择依据分析 3
2.3模型优化策略探讨 4
3数据处理与特征工程 4
3.1数据获取与预处理 4
3.2特征提取方法研究 5
3.3特征选择技术应用 5
4模型构建与性能评估 6
4.1模型架构设计原则 6
4.2训练过程与参数调整 7
4.3性能评估指标体系 7
结论 8
参考文献 9
致谢 10
