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，随着智能电网的发展，传统预测方法难以满足日益复杂的电力系统需求。为此，本文提出基于机器学习的电力负荷预测模型，旨在提高预测精度并增强模型适应性。研究选取典型地区电力负荷数据为样本，采用多种机器学习算法进行对比分析，包括支持向量机、随机森林、长短期记忆网络等，并引入特征工程技术优化输入变量。通过构建多层神经网络结构，融合时间序列特性与气象因素影响，实现对短期和中长期电力负荷的有效预测。实验结果表明，所提模型在不同季节和负荷水平下均表现出优异的预测性能，平均绝对百分比误差低于3%，相比传统方法有显著提升。此外，该模型具备良好的泛化能力，能够适应不同区域电网特点。研究创新点在于结合深度学习与传统机器学习算法优势，提出混合预测框架，有效解决了非线性映射关系建模难题，为电力系统调度提供科学依据，具有重要的理论意义和实用价值。
关键词：电力负荷预测；机器学习；混合预测框架
Abstract
Electric load forecasting is a critical component for ensuring the stable operation and optimizing resource allocation in power systems. With the development of smart grids, traditional forecasting methods have become inadequate to meet the increasingly complex demands of modern power systems. To address this challenge, this study proposes a machine learning-based electric load forecasting model aimed at improving prediction accuracy and enhancing model adaptability. Typical regional electric load data were selected as samples, and multiple machine learning algorithms, including support vector machines, random forests, and long short-term memory networks, were employed for comparative analysis. Feature engineering techniques were introduced to optimize input variables. By constructing a multi-layer neural network structure that integrates time series characteristics with meteorological factors, the proposed model effectively predicts short-term and medium-to-long-term electric loads. Experimental results demonstrate that the model exhibits superior predictive performance across different seasons and load levels, with a mean absolute percentage error below 3%, representing a significant improvement over traditional methods. Additionally, the model possesses excellent generalization capabilities, adapting well to the characteristics of different regional power grids. The innovation of this research lies in combining the advantages of deep learning and traditional machine learning algorithms to propose a hybrid forecasting fr amework, which effectively addresses the challenges of modeling nonlinear mapping relationships. This provides a scientific basis for power system scheduling and holds important theoretical significance and practical value.
Keywords：Electric Load Forecasting; Machine Learning; Hybrid Prediction fr amework
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 本文研究方法 2
二、电力负荷预测基础理论 2
(一) 电力负荷特性分析 2
(二) 传统预测方法综述 3
(三) 机器学习算法原理 3
三、基于机器学习的模型构建 4
(一) 数据预处理技术 4
(二) 特征选择与提取 5
(三) 模型选择与优化 6
四、模型应用与效果评估 6
(一) 实验设计与数据集 6
(二) 预测结果分析 7
(三) 模型性能评价 8
结 论 10
参考文献 11
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，随着智能电网的发展，传统预测方法难以满足日益复杂的电力系统需求。为此，本文提出基于机器学习的电力负荷预测模型，旨在提高预测精度并增强模型适应性。研究选取典型地区电力负荷数据为样本，采用多种机器学习算法进行对比分析，包括支持向量机、随机森林、长短期记忆网络等，并引入特征工程技术优化输入变量。通过构建多层神经网络结构，融合时间序列特性与气象因素影响，实现对短期和中长期电力负荷的有效预测。实验结果表明，所提模型在不同季节和负荷水平下均表现出优异的预测性能，平均绝对百分比误差低于3%，相比传统方法有显著提升。此外，该模型具备良好的泛化能力，能够适应不同区域电网特点。研究创新点在于结合深度学习与传统机器学习算法优势，提出混合预测框架，有效解决了非线性映射关系建模难题，为电力系统调度提供科学依据，具有重要的理论意义和实用价值。
关键词：电力负荷预测；机器学习；混合预测框架
Abstract
Electric load forecasting is a critical component for ensuring the stable operation and optimizing resource allocation in power systems. With the development of smart grids, traditional forecasting methods have become inadequate to meet the increasingly complex demands of modern power systems. To address this challenge, this study proposes a machine learning-based electric load forecasting model aimed at improving prediction accuracy and enhancing model adaptability. Typical regional electric load data were selected as samples, and multiple machine learning algorithms, including support vector machines, random forests, and long short-term memory networks, were employed for comparative analysis. Feature engineering techniques were introduced to optimize input variables. By constructing a multi-layer neural network structure that integrates time series characteristics with meteorological factors, the proposed model effectively predicts short-term and medium-to-long-term electric loads. Experimental results demonstrate that the model exhibits superior predictive performance across different seasons and load levels, with a mean absolute percentage error below 3%, representing a significant improvement over traditional methods. Additionally, the model possesses excellent generalization capabilities, adapting well to the characteristics of different regional power grids. The innovation of this research lies in combining the advantages of deep learning and traditional machine learning algorithms to propose a hybrid forecasting fr amework, which effectively addresses the challenges of modeling nonlinear mapping relationships. This provides a scientific basis for power system scheduling and holds important theoretical significance and practical value.
Keywords：Electric Load Forecasting; Machine Learning; Hybrid Prediction fr amework
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
(三) 本文研究方法 2
二、电力负荷预测基础理论 2
(一) 电力负荷特性分析 2
(二) 传统预测方法综述 3
(三) 机器学习算法原理 3
三、基于机器学习的模型构建 4
(一) 数据预处理技术 4
(二) 特征选择与提取 5
(三) 模型选择与优化 6
四、模型应用与效果评估 6
(一) 实验设计与数据集 6
(二) 预测结果分析 7
(三) 模型性能评价 8
结 论 10
参考文献 11
