电力系统负荷预测中的误差分析与改进措施 -电气工程及其自动化专业

摘要

电力系统负荷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，准确的负荷预测对电力系统的规划、调度与控制具有重要意义。然而，负荷预测过程中存在诸多误差因素，影响预测精度。为此，本文聚焦电力系统负荷预测中的误差分析与改进措施展开研究。基于历史负荷数据、气象信息等多源数据，构建了包含数据预处理、特征选择、模型构建及验证的完整预测框架，采用多种机器学习算法进行对比实验，深入剖析了不同因素引起的误差来源，如数据质量、模型选择、参数设置等，并针对这些误差来源提出相应的改进策略，包括引入数据清洗与异常值处理机制以提高数据质量，优化模型结构与参数调整方法以提升模型泛化能力等。研究结果表明，通过实施上述改进措施，能够有效降低负荷预测误差，提高预测精度，为电力系统负荷预测提供了一种更为可靠的方法，在理论研究方面丰富了电力系统负荷预测误差分析体系，在实际应用中可为电力部门制定精准的调度计划提供有力支持。

关键词：电力系统负荷预测误差分析机器学习算法

Abstract

Load forecasting in power systems is a critical component for ensuring the safe and stable operation of the grid and optimizing resource allocation, and accurate load forecasting plays a significant role in the planning, scheduling, and control of power systems. However, numerous error factors exist during the load forecasting process, which affect the accuracy of predictions. This study focuses on the analysis of errors and improvement measures in load forecasting within power systems. Based on multi-source data such as historical load data and meteorological information, a comprehensive forecasting fr amework encompassing data preprocessing, feature selection, model construction, and validation was established. Multiple machine learning algorithms were employed for comparative experiments to thoroughly analyze the sources of errors caused by various factors, including data quality, model selection, and parameter settings. Corresponding improvement strategies were proposed for these error sources, such as introducing data cleaning and outlier handling mechanisms to enhance data quality, and optimizing model structures and parameter adjustment methods to improve model generalization capabilities. The research results indicate that implementing these improvement measures can effectively reduce load forecasting errors and increase prediction accuracy, providing a more reliable method for load forecasting in power systems. This study enriches the theoretical fr amework of error analysis in power system load forecasting and offers substantial support for power departments in formulating precise scheduling plans in practical applications.

Keyword:Power System Load Forecasting Error Analysis Machine Learning Algorithms

1绪论 1

1.1电力系统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负荷预测误差来源分析 2

2.1数据采集误差分析 2

2.2模型选择误差探讨 3

2.3外部因素影响评估 3

3误差量化与评价方法 4

3.1常用误差指标解析 4

3.2误差传播机制研究 5