摘 要
电力系统负荷预测是保障电力系统安全稳定运行和经济高效调度的重要基础，准确的负荷预测依赖于高质量的数据。然而，实际电力系统中存在数据缺失、噪声干扰、异常值等问题，严重影响预测精度。为此，本文聚焦电力系统负荷预测中的数据预处理技术展开研究，旨在通过有效的数据预处理方法提高负荷预测的准确性。研究基于对电力系统负荷数据特征的深入分析，提出一种融合多种算法的数据预处理框架，该框架首先采用基于时间序列特性的数据清洗算法去除噪声并填补缺失值；接着利用小波变换分解原始负荷序列以提取不同频率成分，并针对各成分特性分别进行处理；最后引入深度学习模型对预处理后的数据进行特征学习与建模。实验结果表明，经此框架预处理后的数据用于负荷预测时，在多个评价指标上均优于传统方法，不仅提高了预测精度，还增强了模型的鲁棒性。本文提出的框架创新性地将多种算法有机结合，为电力系统负荷预测提供了新的思路与方法，对提升电力系统的智能化水平具有重要意义。
关键词：电力系统负荷预测 数据预处理 时间序列清洗
Abstract
Load forecasting in power systems is a critical foundation for ensuring the safe, stable operation and economically efficient dispatch of power systems, and accurate load forecasting relies on high-quality data. However, practical power systems often encounter issues such as data missingness, noise interference, and outliers, which significantly affect forecasting accuracy. To address these challenges, this study focuses on data preprocessing techniques for power system load forecasting, aiming to enhance forecasting accuracy through effective data preprocessing methods. Based on an in-depth analysis of the characteristics of power system load data, a hybrid data preprocessing fr amework is proposed. This fr amework initially employs time-series-based data cleaning algorithms to remove noise and impute missing values. Subsequently, wavelet transform is utilized to decompose the original load series to extract components at different frequencies, with each component processed according to its specific characteristics. Finally, deep learning models are introduced to perform feature learning and modeling on the preprocessed data. Experimental results demonstrate that using data processed by this fr amework for load forecasting outperforms traditional methods across multiple evaluation metrics, not only improving forecasting accuracy but also enhancing model robustness. The proposed fr amework innovatively integrates multiple algorithms, offering new approaches and methodologies for power system load forecasting, which is of significant importance for advancing the intelligence level of power systems.
Keyword:Electricity System Load Forecasting Data Preprocessing Time Series Cleaning
目 录
1绪论 1
1.1电力系统负荷预测背景与意义 1
1.2数据预处理技术研究现状综述 1
1.3本文研究方法与创新点 2
2数据采集与清洗技术 2
2.1负荷数据采集方法分析 2
2.2异常数据识别与处理 3
2.3缺失数据填补策略研究 3
3数据特征提取与转换 4
3.1特征选择与降维方法 4
3.2时间序列特征构建 5
3.3数据标准化与归一化 5
4预处理效果评估与优化 6
4.1预处理模型性能评价 6
4.2不同算法对比分析 7
4.3预处理参数优化策略 7
结论 8
参考文献 9
致谢 10
