摘 要
随着全球能源需求的不断增长和环境问题的日益严峻，电力需求侧管理成为实现能源高效利用与可持续发展的重要途径。本研究旨在基于大数据分析构建电力需求侧管理策略，以提高电力系统的运行效率和可靠性。通过整合来自智能电表、气象数据、用户行为等多源异构数据，采用机器学习算法挖掘电力消费模式与影响因素之间的内在联系。研究建立了包含短期预测、长期规划及实时调控在内的多层次电力需求侧管理体系，并提出了一种融合深度学习与优化理论的混合模型用于精准负荷预测。结果表明，该方法能够有效提升预测精度达15%以上，同时实现了对不同类型用户的精细化分类管理。此外，针对居民、商业和工业三大用电领域分别制定了差异化的响应机制，确保了供需平衡的同时降低了系统峰谷差。本研究创新性地将大数据技术应用于电力需求侧管理实践，为电力企业提供了科学决策依据，也为相关政策制定者提供了有益参考，推动了智慧能源体系建设与发展。
关键词：电力需求侧管理 大数据分析 机器学习
Abstract
With the continuous growth of global energy demand and the increasing severity of environmental issues, demand-side management (DSM) of electricity has become a crucial approach to achieving efficient energy utilization and sustainable development. This study aims to construct DSM strategies based on big data analysis to enhance the operational efficiency and reliability of power systems. By integrating multi-source heterogeneous data from smart meters, meteorological information, and user behavior, machine learning algorithms are employed to uncover the intrinsic relationships between electricity consumption patterns and influencing factors. A multi-level DSM fr amework encompassing short-term forecasting, long-term planning, and real-time control is established, along with a hybrid model that combines deep learning and optimization theory for precise load prediction. The results indicate that this method can improve prediction accuracy by over 15%, while also enabling refined classification management of different types of users. Furthermore, differentiated response mechanisms are developed for residential, commercial, and industrial sectors, ensuring supply-demand balance and reducing peak-to-valley differences in the system. Innovatively applying big data technology to DSM practices, this research provides scientific decision-making support for power companies and valuable references for policy makers, promoting the construction and development of smart energy systems.
Keyword:Power Demand Side Management Big Data Analysis Machine Learning
目 录
引言 1
1电力需求侧管理的现状与挑战 1
1.1电力需求侧管理概述 1
1.2当前管理策略存在的问题 2
1.3大数据分析的应用潜力 2
2大数据分析技术在电力需求侧的应用 3
2.1数据收集与预处理方法 3
2.2关键数据分析技术介绍 3
2.3应用案例分析与评价 4
3基于大数据的需求预测模型构建 4
3.1预测模型的设计原则 4
3.2主要影响因素分析 5
3.3模型验证与优化方案 5
4大数据驱动的电力需求响应机制 6
4.1用户行为模式挖掘 6
4.2激励机制设计思路 6
4.3实施效果评估体系 7
结论 7
参考文献 9
致谢 10
