摘 要
随着电力系统规模的不断扩大和复杂性的增加，准确的短期负荷预测对于电力系统的稳定运行、优化调度及资源合理配置具有重要意义。本研究旨在利用大数据技术提升电力系统短期负荷预测精度，基于海量历史负荷数据、气象数据等多源异构数据构建预测模型。采用深度学习算法中的长短期记忆网络（LSTM），充分挖掘时间序列数据中的长期依赖关系，并引入注意力机制以增强对关键特征的关注度。同时，为解决数据噪声问题，运用小波变换进行预处理，有效提取负荷数据的主要特征。实验结果表明，所提方法相较于传统预测模型在预测精度上有显著提高，平均绝对百分比误差降低约15%。该方法能够适应不同季节、天气条件下的负荷变化规律，具备较强的泛化能力。本研究创新性地将大数据分析与深度学习相结合应用于电力系统短期负荷预测领域，不仅提高了预测准确性，还为电力系统的智能化发展提供了新的思路和技术支持，有助于实现更加精准高效的电力调度与管理。
关键词：短期负荷预测 长短期记忆网络 大数据技术
Abstract
As the scale and complexity of power systems continue to expand, accurate short-term load forecasting plays a crucial role in ensuring stable operation, optimizing scheduling, and achieving rational resource allocation. This study aims to enhance the accuracy of short-term load forecasting in power systems by leveraging big data technology. A predictive model is constructed based on massive historical load data and meteorological data from multiple heterogeneous sources. The Long Short-Term Memory (LSTM) network, a deep learning algorithm, is employed to fully exploit the long-term dependencies within time series data. Additionally, an attention mechanism is introduced to increase the focus on key features. To address data noise issues, wavelet transform preprocessing is utilized, effectively extracting the main characteristics of load data. Experimental results demonstrate that the proposed method significantly improves prediction accuracy compared to traditional models, with a reduction of approximately 15% in mean absolute percentage error. This approach can adapt to load variation patterns under different seasonal and weather conditions, exhibiting strong generalization capability. Innovatively combining big data analytics with deep learning for application in short-term load forecasting in power systems, this research not only enhances prediction accuracy but also provides new insights and technical support for the intelligent development of power systems, contributing to more precise and efficient power dispatching and management.
Keyword:Short-Term Load Forecasting Long Short-Term Memory Network Big Data Technology
目 录
1绪论 1
1.1电力系统短期负荷预测的背景与意义 1
1.2国内外研究现状综述 1
1.3研究方法与技术路线 2
2大数据在电力系统中的应用基础 2
2.1电力大数据的特点与来源 2
2.2数据预处理与质量控制 3
2.3特征选择与降维方法 3
3短期负荷预测模型构建 4
3.1常用预测算法比较 4
3.2模型架构设计原则 5
3.3模型训练与优化策略 5
4预测结果分析与验证 6
4.1实验环境与数据集介绍 6
4.2预测精度评估指标 7
4.3结果分析与讨论 7
结论 8
参考文献 10
致谢 11
