摘 要
电力系统负荷预测是保障电网安全稳定运行和经济调度的关键环节，随着智能电网的发展和可再生能源的接入，传统预测方法面临诸多挑战。基于机器学习的电力系统负荷预测方法旨在解决上述问题，提高预测精度与可靠性。本研究以提升短期负荷预测准确性为目标，构建了融合多种机器学习算法的预测框架，包括支持向量机、随机森林及深度神经网络等，并引入注意力机制优化模型结构。针对不同场景需求，采用多源数据融合技术整合气象、历史负荷等多维度特征信息，通过对比实验验证所提方法的有效性。结果表明，在相同测试集下，相较于传统统计模型，该方法平均绝对百分比误差降低约20%，且对极端天气条件具有更强适应能力。此外，提出一种基于自适应权重调整的集成策略，进一步提升了预测鲁棒性。研究表明，基于机器学习的负荷预测方案不仅能够有效应对复杂工况变化，还为电力系统智能化转型提供了新的思路和技术手段，为实现精准调度和节能减排目标奠定了坚实基础。
关键词：电力系统负荷预测 机器学习 多源数据融合
Abstract
Load forecasting in power systems is a critical component for ensuring the safe, stable operation and economic dispatch of the grid. With the development of smart grids and the integration of renewable energy sources, traditional forecasting methods face numerous challenges. This study aims to address these issues by employing machine learning-based approaches to improve forecasting accuracy and reliability. Specifically, this research focuses on enhancing short-term load forecasting accuracy through the development of a hybrid prediction fr amework that integrates multiple machine learning algorithms, including Support Vector Machines (SVM), Random Forests, and Deep Neural Networks, with the incorporation of attention mechanisms to optimize model architecture. To meet diverse scenario requirements, multi-source data fusion techniques are utilized to integrate multidimensional feature information such as meteorological data and historical load data. Comparative experiments validate the effectiveness of the proposed method. Results indicate that, under identical test conditions, the average absolute percentage error is reduced by approximately 20% compared to traditional statistical models, and the method demonstrates superior adaptability to extreme weather conditions. Additionally, an adaptive weight adjustment ensemble strategy is proposed, further enhancing the robustness of predictions. The findings suggest that machine learning-based load forecasting not only effectively handles complex operational changes but also provides new insights and technical means for the intelligent transformation of power systems, laying a solid foundation for achieving precise dispatch and energy conservation goals.
Keyword:Power System Load Forecasting Machine Learning Multi-Source Data Fusion
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2电力系统负荷预测基础 2
2.1负荷预测基本概念 2
2.2传统预测方法分析 3
2.3机器学习在电力系统的应用 3
3基于机器学习的模型构建 4
3.1数据预处理技术 4
3.2特征选择与提取 4
3.3模型选择与优化 5
4实验验证与结果分析 6
4.1实验环境搭建 6
4.2预测性能评估 6
4.3结果对比与讨论 7
结论 8
参考文献 10
致谢 11
电力系统负荷预测是保障电网安全稳定运行和经济调度的关键环节，随着智能电网的发展和可再生能源的接入，传统预测方法面临诸多挑战。基于机器学习的电力系统负荷预测方法旨在解决上述问题，提高预测精度与可靠性。本研究以提升短期负荷预测准确性为目标，构建了融合多种机器学习算法的预测框架，包括支持向量机、随机森林及深度神经网络等，并引入注意力机制优化模型结构。针对不同场景需求，采用多源数据融合技术整合气象、历史负荷等多维度特征信息，通过对比实验验证所提方法的有效性。结果表明，在相同测试集下，相较于传统统计模型，该方法平均绝对百分比误差降低约20%，且对极端天气条件具有更强适应能力。此外，提出一种基于自适应权重调整的集成策略，进一步提升了预测鲁棒性。研究表明，基于机器学习的负荷预测方案不仅能够有效应对复杂工况变化，还为电力系统智能化转型提供了新的思路和技术手段，为实现精准调度和节能减排目标奠定了坚实基础。
关键词：电力系统负荷预测 机器学习 多源数据融合
Abstract
Load forecasting in power systems is a critical component for ensuring the safe, stable operation and economic dispatch of the grid. With the development of smart grids and the integration of renewable energy sources, traditional forecasting methods face numerous challenges. This study aims to address these issues by employing machine learning-based approaches to improve forecasting accuracy and reliability. Specifically, this research focuses on enhancing short-term load forecasting accuracy through the development of a hybrid prediction fr amework that integrates multiple machine learning algorithms, including Support Vector Machines (SVM), Random Forests, and Deep Neural Networks, with the incorporation of attention mechanisms to optimize model architecture. To meet diverse scenario requirements, multi-source data fusion techniques are utilized to integrate multidimensional feature information such as meteorological data and historical load data. Comparative experiments validate the effectiveness of the proposed method. Results indicate that, under identical test conditions, the average absolute percentage error is reduced by approximately 20% compared to traditional statistical models, and the method demonstrates superior adaptability to extreme weather conditions. Additionally, an adaptive weight adjustment ensemble strategy is proposed, further enhancing the robustness of predictions. The findings suggest that machine learning-based load forecasting not only effectively handles complex operational changes but also provides new insights and technical means for the intelligent transformation of power systems, laying a solid foundation for achieving precise dispatch and energy conservation goals.
Keyword:Power System Load Forecasting Machine Learning Multi-Source Data Fusion
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2电力系统负荷预测基础 2
2.1负荷预测基本概念 2
2.2传统预测方法分析 3
2.3机器学习在电力系统的应用 3
3基于机器学习的模型构建 4
3.1数据预处理技术 4
3.2特征选择与提取 4
3.3模型选择与优化 5
4实验验证与结果分析 6
4.1实验环境搭建 6
4.2预测性能评估 6
4.3结果对比与讨论 7
结论 8
参考文献 10
致谢 11
