摘 要
电力负荷预测是保障电力系统稳定运行和优化资源配置的关键环节，传统预测方法在应对复杂多变的电力负荷特性时存在局限性。为此，本文构建基于机器学习的电力负荷预测模型，旨在提高预测精度与可靠性。研究选取多种典型机器学习算法，包括支持向量机、随机森林、梯度提升决策树等，通过对比分析确定最优模型结构。针对电力负荷数据特点，提出融合时间序列特征与气象因素的综合输入方案，并引入注意力机制增强模型对关键特征的学习能力。实验结果表明，所构建模型在不同场景下的预测误差均显著低于传统方法，平均绝对百分比误差降低约30%。该模型能够有效捕捉电力负荷的非线性变化规律，为电力系统的调度规划提供科学依据，具有重要的理论意义和实用价值，为智能电网建设提供了新的技术支撑。
关键词：电力负荷预测；机器学习；支持向量机
Abstract
Electric load forecasting is a critical component for ensuring the stable operation and optimizing resource allocation in power systems. Traditional forecasting methods exhibit limitations when dealing with the complex and dynamic characteristics of electric loads. To address this issue, this study develops a machine learning-based electric load forecasting model aimed at improving prediction accuracy and reliability. Multiple typical machine learning algorithms, including support vector machines, random forests, and gradient boosting decision trees, are selected for analysis to determine the optimal model structure. Considering the characteristics of electric load data, an integrated input scheme that combines time series features with meteorological factors is proposed, and an attention mechanism is introduced to enhance the model's ability to learn key features. Experimental results demonstrate that the constructed model achieves significantly lower prediction errors across different scenarios compared to traditional methods, with the mean absolute percentage error reduced by approximately 30%. This model effectively captures the nonlinear patterns of electric load variations, providing a scientific basis for power system scheduling and planning. It holds significant theoretical implications and practical value, offering new technological support for the development of smart grids.
Keywords: Electric Load Forecasting；Machine Learning；Support Vector Machine
目 录
引言 1
一、电力负荷预测研究背景与意义 1
（一）电力系统运行需求分析 1
（二）国内外研究现状综述 2
（三）研究目标与主要内容 2
二、机器学习方法在电力负荷预测中的应用 2
（一）常用机器学习算法介绍 3
（二）算法选择依据与适用性 3
（三）数据预处理与特征提取 3
三、电力负荷预测模型构建 4
（一）模型架构设计原则 4
（二）关键参数优化方法 4
（三）模型训练与验证流程 5
四、电力负荷预测模型性能评估 5
（一）评估指标体系建立 5
（二）实验结果对比分析 6
（三）性能改进策略探讨 6
结 论 7
致 谢 8
参考文献 9
电力负荷预测是保障电力系统稳定运行和优化资源配置的关键环节，传统预测方法在应对复杂多变的电力负荷特性时存在局限性。为此，本文构建基于机器学习的电力负荷预测模型，旨在提高预测精度与可靠性。研究选取多种典型机器学习算法，包括支持向量机、随机森林、梯度提升决策树等，通过对比分析确定最优模型结构。针对电力负荷数据特点，提出融合时间序列特征与气象因素的综合输入方案，并引入注意力机制增强模型对关键特征的学习能力。实验结果表明，所构建模型在不同场景下的预测误差均显著低于传统方法，平均绝对百分比误差降低约30%。该模型能够有效捕捉电力负荷的非线性变化规律，为电力系统的调度规划提供科学依据，具有重要的理论意义和实用价值，为智能电网建设提供了新的技术支撑。
关键词：电力负荷预测；机器学习；支持向量机
Abstract
Electric load forecasting is a critical component for ensuring the stable operation and optimizing resource allocation in power systems. Traditional forecasting methods exhibit limitations when dealing with the complex and dynamic characteristics of electric loads. To address this issue, this study develops a machine learning-based electric load forecasting model aimed at improving prediction accuracy and reliability. Multiple typical machine learning algorithms, including support vector machines, random forests, and gradient boosting decision trees, are selected for analysis to determine the optimal model structure. Considering the characteristics of electric load data, an integrated input scheme that combines time series features with meteorological factors is proposed, and an attention mechanism is introduced to enhance the model's ability to learn key features. Experimental results demonstrate that the constructed model achieves significantly lower prediction errors across different scenarios compared to traditional methods, with the mean absolute percentage error reduced by approximately 30%. This model effectively captures the nonlinear patterns of electric load variations, providing a scientific basis for power system scheduling and planning. It holds significant theoretical implications and practical value, offering new technological support for the development of smart grids.
Keywords: Electric Load Forecasting；Machine Learning；Support Vector Machine
目 录
引言 1
一、电力负荷预测研究背景与意义 1
（一）电力系统运行需求分析 1
（二）国内外研究现状综述 2
（三）研究目标与主要内容 2
二、机器学习方法在电力负荷预测中的应用 2
（一）常用机器学习算法介绍 3
（二）算法选择依据与适用性 3
（三）数据预处理与特征提取 3
三、电力负荷预测模型构建 4
（一）模型架构设计原则 4
（二）关键参数优化方法 4
（三）模型训练与验证流程 5
四、电力负荷预测模型性能评估 5
（一）评估指标体系建立 5
（二）实验结果对比分析 6
（三）性能改进策略探讨 6
结 论 7
致 谢 8
参考文献 9
