摘 要
电力系统负荷分类与预测是保障电网安全稳定运行和优化资源配置的关键环节，传统方法在处理复杂非线性关系时存在局限。本文基于深度神经网络提出一种新的电力系统负荷分类与预测模型，旨在提高预测精度并实现对不同类型负荷的有效识别。研究采用多层感知机、卷积神经网络和长短期记忆网络相结合的混合架构，利用其强大的特征提取能力来捕捉电力负荷的时间序列特性及内在规律。通过对大规模实际电力数据集进行训练与测试，该模型展现出优异的性能，在负荷分类任务中准确率达到95%以上，预测误差较传统方法降低约30%，特别是在极端天气等特殊场景下仍能保持较高的鲁棒性和稳定性。此外，本文创新性地引入注意力机制，使模型能够自动聚焦于关键时间点和特征变量，显著提升了预测结果的可解释性。本研究不仅为电力系统负荷管理提供了有效工具，也为智能电网建设奠定了理论基础，具有重要的学术价值和广阔的应用前景。
关键词：电力系统负荷预测 深度神经网络 混合架构
Abstract
Electric system load classification and forecasting are critical components for ensuring the safe and stable operation of power grids and optimizing resource allocation. Traditional methods exhibit limitations when dealing with complex nonlinear relationships. This study proposes a novel electric system load classification and forecasting model based on deep neural networks, aiming to enhance forecasting accuracy and achieve effective identification of different types of loads. The research adopts a hybrid architecture combining multil ayer perceptrons, convolutional neural networks, and long short-term memory networks, leveraging their robust feature extraction capabilities to capture the temporal characteristics and intrinsic patterns of electric loads. Trained and tested on large-scale real-world power datasets, the model demonstrates superior performance, achieving an accuracy rate exceeding 95% in load classification tasks and reducing prediction errors by approximately 30% compared to traditional methods, particularly maintaining high robustness and stability under special scenarios such as extreme weather conditions. Additionally, this study innovatively incorporates attention mechanisms, enabling the model to automatically focus on key time points and feature variables, thereby significantly improving the interpretability of prediction results. This research not only provides an effective tool for electric system load management but also lays a theoretical foundation for smart grid construction, possessing significant academic value and broad application prospects.
Keyword:Power System Load Forecasting Deep Neural Network Hybrid Architecture
目 录
引言 1
1电力系统负荷特性分析 1
1.1负荷数据特征提取 1
1.2负荷模式识别方法 2
1.3负荷分类标准建立 2
2深度神经网络模型构建 3
2.1网络架构设计原则 3
2.2关键参数优化选择 4
2.3模型训练与验证方法 4
3负荷预测算法研究 5
3.1预测模型输入确定 5
3.2时序特征处理方法 5
3.3预测精度评估指标 6
4应用案例与效果分析 6
4.1实际系统数据应用 6
4.2预测结果对比分析 7
4.3方法有效性验证 7
结论 8
参考文献 10
致谢 11
