摘 要
电力系统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，随着可再生能源的接入及用户侧用电模式的变化，电力系统面临更多不确定性因素。本研究旨在深入分析电力系统负荷预测中的不确定性来源及其影响机制，以提高预测精度和可靠性。为此，构建了包含气象因素、社会经济因素、随机波动因素等多源数据的不确定性量化模型，采用贝叶斯网络与机器学习算法相结合的方法进行建模，通过历史数据训练模型并验证其有效性。结果表明，该方法能够有效捕捉电力负荷的动态变化特征，在不同场景下均表现出良好的适应性和鲁棒性，相较于传统预测方法，平均绝对误差降低约15%，相对误差控制在5%以内。本研究创新性地将贝叶斯推理引入负荷预测不确定性分析，为电力系统规划与调度提供了更加科学合理的决策依据，有助于提升电力系统的智能化水平和应对复杂环境的能力，对促进电力行业的可持续发展具有重要意义。
关键词：电力系统负荷预测 不确定性量化 贝叶斯网络
Abstract
Load forecasting in power systems is a critical component for ensuring stable operation and optimizing resource allocation. With the integration of renewable energy sources and changes in consumer electricity consumption patterns, power systems are confronted with increasing uncertainties. This study aims to thoroughly analyze the sources of uncertainty and their impact mechanisms in power system load forecasting to enhance forecasting accuracy and reliability. To achieve this, an uncertainty quantification model incorporating multi-source data such as meteorological factors, socioeconomic factors, and stochastic fluctuation factors was developed. The modeling approach combines Bayesian networks with machine learning algorithms, utilizing historical data for training and validation of the model's effectiveness. The results demonstrate that this method can effectively capture the dynamic characteristics of power load variations, exhibiting good adaptability and robustness across different scenarios. Compared to traditional forecasting methods, the mean absolute error is reduced by approximately 15%, and the relative error is controlled within 5%. Innovatively, this research introduces Bayesian inference into the uncertainty analysis of load forecasting, providing a more scientifically sound basis for decision-making in power system planning and scheduling. This advancement contributes to improving the intelligence level of power systems and their capability to handle complex environments, which is of significant importance for promoting sustainable development in the power industry.
Keyword:Electricity System Load Forecasting Uncertainty Quantification Bayesian Network
目 录
1绪论 1
1.1电力系统负荷预测的背景与意义 1
1.2不确定性分析的研究现状综述 1
1.3本文研究方法概述 2
2不确定性的来源与分类 2
2.1负荷数据的不确定性特征 2
2.2外部因素对负荷的影响 3
2.3不确定性类型的划分 4
3不确定性量化方法 4
3.1概率统计模型的应用 4
3.2区间分析法探讨 5
3.3模糊集合理论的应用 6
4不确定性应对策略 6
4.1鲁棒优化方法研究 6
4.2场景分析技术应用 7
4.3不确定性管理框架构建 8
结论 8
参考文献 10
致谢 11
