摘 要
随着智能电网的快速发展和清洁能源的广泛应用，电力负荷的复杂性和不确定性显著增加，对电力负荷的准确分类与预测提出了更高要求。基于深度学习的电力负荷分类与预测方法，以其强大的数据处理能力和模型泛化能力，成为当前研究的热点。本文旨在探讨深度学习在电力负荷分类与预测中的应用，通过构建高效的深度学习模型，实现对电力负荷的精准分类与预测。本文介绍了电力负荷分类与预测的重要性及挑战。电力负荷的准确分类有助于电力公司更好地理解用户需求，优化电力资源配置；而精确的负荷预测则是电力系统调度、规划及市场交易的基础。然而，电力负荷受多种因素影响，具有高度的非线性和时变性，传统预测方法难以达到满意的预测精度。本文详细阐述了基于深度学习的电力负荷分类与预测方法。在分类方面，利用卷积神经网络（CNN）等深度学习模型，通过对电力负荷数据的特征提取和分类学习，实现对不同类型负荷的准确识别。在预测方面，则采用循环神经网络（RNN）及其变体，如长短期记忆网络（LSTM）和门控循环单元（GRU）等，利用这些模型在处理时间序列数据上的优势，捕捉电力负荷的动态变化特征，实现对未来负荷的精准预测。
关键词：深度学习 电力负荷分类 电力负荷预测
Abstract
With the rapid development of smart grid and the wide application of clean energy, the complexity and uncertainty of power load increase significantly, which puts forward higher requirements for accurate classification and prediction of power load. The power load classification and prediction method based on deep learning has become the focus of current research because of its powerful data processing ability and model generalization ability. This paper aims to explore the application of deep learning in power load classification and prediction, and to achieve accurate classification and prediction of power load by building an efficient deep learning model. This paper introduces the importance and challenge of power load classification and forecasting. The accurate classification of power load is helpful for power companies to better understand the needs of users and optimize the allocation of power resources. Accurate load forecasting is the basis of power system scheduling, planning and market trading. However, the power load is affected by many factors and is highly nonlinear and time-varying, so it is difficult for traditional forecasting methods to achieve satisfactory forecasting accuracy. This paper describes in detail the power load classification and prediction method based on deep learning. In terms of classification, deep learning models such as convolutional neural network (CNN) are used to achieve accurate identification of different types of loads through feature extraction and classification learning of power load data. In terms of forecasting, recurrent neural network (RNN) and its variants, such as long short-term memory network (LSTM) and gated cycle unit (GRU), are used to capture the dynamic change characteristics of power load by utilizing the advantages of these models in processing time series data, so as to achieve accurate prediction of future load.
Keyword:Deep learning Power load classification Power load forecasting
目 录
1引言 1
2相关技术与理论基础 1
2.1电力负荷特性分析 1
2.2深度学习基本原理 1
2.3负荷分类与预测技术现状 2
3电力负荷数据预处理与特征提取 2
3.1数据收集与清洗 2
3.2数据预处理方法 3
3.3特征提取与选择 4
4深度学习模型构建与训练 4
4.1模型结构设计 4
4.2模型训练与优化 5
4.3模型评估与验证 6
5电力负荷分类与预测实验 6
5.1实验设计与设置 6
5.2实验结果与分析 7
5.3模型对比与讨论 7
6结论 8
参考文献 9
致谢 10
