摘 要
随着电力系统规模的不断扩大和复杂性的增加，准确的短期电力负荷预测对于保障电力系统的安全稳定运行、提高经济效益具有重要意义。本研究旨在构建基于机器学习的短期电力负荷预测模型，以提高预测精度并降低不确定性。针对传统预测方法在处理非线性关系时存在的不足，提出了一种融合多种机器学习算法的集成预测框架，该框架结合了长短期记忆网络（LSTM）对时间序列数据的记忆特性以及XGBoost算法在特征选择与非线性拟合方面的优势，并引入注意力机制来增强模型对关键特征的关注度。通过对比分析不同算法组合下的预测效果，实验结果表明所提出的集成模型能够有效捕捉电力负荷的变化规律，在多个评价指标上均优于单一算法模型，平均绝对百分比误差（MAPE）降低了约15%。此外，本研究还探讨了温度、湿度等气象因素以及节假日效应等因素对预测结果的影响，进一步验证了模型的鲁棒性和泛化能力。研究表明，基于机器学习的集成预测方法为短期电力负荷预测提供了一种新的思路和技术手段，不仅提高了预测精度，而且为电力系统的优化调度提供了有力支持。
关键词：短期电力负荷预测 机器学习集成模型 长短期记忆网络
Abstract
With the continuous expansion and increasing complexity of power systems, accurate short-term electricity load forecasting plays a crucial role in ensuring the safe and stable operation of power systems and enhancing economic efficiency. This study aims to develop a machine learning-based short-term electricity load forecasting model to improve prediction accuracy and reduce uncertainty. Addressing the limitations of traditional forecasting methods in handling nonlinear relationships, this research proposes an ensemble prediction fr amework that integrates multiple machine learning algorithms. The fr amework leverages the memory characteristics of Long Short-Term Memory (LSTM) networks for time series data and the advantages of XGBoost in feature selection and nonlinear fitting, while incorporating attention mechanisms to enhance the model's focus on key features. Experimental results, through comparative analysis of prediction performance under different algorithm combinations, demonstrate that the proposed ensemble model effectively captures the patterns of electricity load changes and outperforms single-algorithm models across multiple evaluation metrics, reducing the Mean Absolute Percentage Error (MAPE) by approximately 15%. Additionally, this study investigates the impact of meteorological factors such as temperature and humidity, as well as holiday effects, on the forecasting outcomes, further validating the robustness and generalization capability of the model. The research indicates that the machine learning-based ensemble prediction approach provides a new perspective and technical means for short-term electricity load forecasting, not only improving prediction accuracy but also offering strong support for the optimal scheduling of power systems.
Keyword:Short-Term Power Load Forecasting Machine Learning Ensemble Model Long Short-Term Memory Network
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短期电力负荷特性分析 2
2.1负荷数据特征提取 2
2.2影响因素分析 3
2.3数据预处理方法 3
3机器学习模型构建 4
3.1模型选择依据 4
3.2特征工程设计 5
3.3模型训练与优化 5
4预测结果评估与应用 6
4.1评估指标体系 6
4.2结果对比分析 7
4.3实际应用案例 7
结论 8
参考文献 9
致谢 10
