摘 要
随着智能电网规模的不断扩大和复杂度的增加，其运行过程中产生了海量且多源异构的大数据，对这些数据的有效挖掘与利用成为提升智能电网决策支持能力的关键。为此，本文聚焦于智能电网中的大数据挖掘与决策支持技术研究，旨在构建高效的数据处理框架以实现精准决策。通过融合数据预处理、特征提取等方法对原始数据进行清洗与转换，基于机器学习算法建立多种预测模型并优化，如采用深度神经网络提高负荷预测精度。同时创新性地引入知识图谱技术，将电力设备运行状态、故障信息等关联起来形成语义网络，辅助故障诊断与风险评估。结果表明，所提方法能够显著提升数据挖掘效率与决策准确性，在智能电网调度、运维等方面具有重要应用价值，为智能电网的安全稳定运行提供了有力的技术支撑。
关键词：智能电网；大数据挖掘；决策支持技术
Abstract
With the continuous expansion and increasing complexity of smart grids, massive volumes of multi-source heterogeneous big data are generated during their operation. Effective mining and utilization of these data have become critical for enhancing decision support capabilities in smart grids. This study focuses on big data mining and decision support technologies in smart grids, aiming to construct an efficient data processing fr amework to achieve precise decision-making. By integrating methods such as data preprocessing and feature extraction, raw data are cleaned and transformed. Various predictive models are established and optimized based on machine learning algorithms, including the use of deep neural networks to improve load forecasting accuracy. Innovatively, knowledge graph technology is introduced to link operational status of power equipment and fault information, forming a semantic network that aids in fault diagnosis and risk assessment. The results demonstrate that the proposed methods significantly enhance data mining efficiency and decision accuracy, offering substantial application value in smart grid scheduling and maintenance, thereby providing robust technical support for the safe and stable operation of smart grids.
Keywords: Smart Grid；Big Data Mining；Decision Support Technology
目 录
引言 1
一、智能电网大数据基础 1
（一）智能电网数据特征分析 1
（二）数据采集与预处理技术 2
（三）数据存储与管理架构 2
二、大数据挖掘关键技术 3
（一）数据挖掘算法选择 3
（二）特征提取与降维方法 3
（三）异常检测与模式识别 4
三、决策支持系统构建 4
（一）决策模型建立方法 4
（二）实时决策支持机制 4
（三）风险评估与预警体系 5
四、技术应用与案例分析 5
（一）负荷预测应用场景 5
（二）故障诊断实际案例 6
（三）运营优化实践探索 6
结 论 7
致 谢 8
参考文献 9
随着智能电网规模的不断扩大和复杂度的增加，其运行过程中产生了海量且多源异构的大数据，对这些数据的有效挖掘与利用成为提升智能电网决策支持能力的关键。为此，本文聚焦于智能电网中的大数据挖掘与决策支持技术研究，旨在构建高效的数据处理框架以实现精准决策。通过融合数据预处理、特征提取等方法对原始数据进行清洗与转换，基于机器学习算法建立多种预测模型并优化，如采用深度神经网络提高负荷预测精度。同时创新性地引入知识图谱技术，将电力设备运行状态、故障信息等关联起来形成语义网络，辅助故障诊断与风险评估。结果表明，所提方法能够显著提升数据挖掘效率与决策准确性，在智能电网调度、运维等方面具有重要应用价值，为智能电网的安全稳定运行提供了有力的技术支撑。
关键词：智能电网；大数据挖掘；决策支持技术
Abstract
With the continuous expansion and increasing complexity of smart grids, massive volumes of multi-source heterogeneous big data are generated during their operation. Effective mining and utilization of these data have become critical for enhancing decision support capabilities in smart grids. This study focuses on big data mining and decision support technologies in smart grids, aiming to construct an efficient data processing fr amework to achieve precise decision-making. By integrating methods such as data preprocessing and feature extraction, raw data are cleaned and transformed. Various predictive models are established and optimized based on machine learning algorithms, including the use of deep neural networks to improve load forecasting accuracy. Innovatively, knowledge graph technology is introduced to link operational status of power equipment and fault information, forming a semantic network that aids in fault diagnosis and risk assessment. The results demonstrate that the proposed methods significantly enhance data mining efficiency and decision accuracy, offering substantial application value in smart grid scheduling and maintenance, thereby providing robust technical support for the safe and stable operation of smart grids.
Keywords: Smart Grid；Big Data Mining；Decision Support Technology
目 录
引言 1
一、智能电网大数据基础 1
（一）智能电网数据特征分析 1
（二）数据采集与预处理技术 2
（三）数据存储与管理架构 2
二、大数据挖掘关键技术 3
（一）数据挖掘算法选择 3
（二）特征提取与降维方法 3
（三）异常检测与模式识别 4
三、决策支持系统构建 4
（一）决策模型建立方法 4
（二）实时决策支持机制 4
（三）风险评估与预警体系 5
四、技术应用与案例分析 5
（一）负荷预测应用场景 5
（二）故障诊断实际案例 6
（三）运营优化实践探索 6
结 论 7
致 谢 8
参考文献 9
