摘 要
随着电力系统规模的不断扩大和可再生能源的广泛应用，准确预测季节性电力负荷对于电力系统的稳定运行、资源优化配置及节能减排具有重要意义。为此，本文旨在构建一种高效的季节性电力负荷预测模型优化方法，以提高预测精度并增强模型适应性。首先，基于对历史电力负荷数据的深入分析，结合气象因素、社会经济指标等多源数据，提出了一种融合深度学习与传统统计方法的混合预测框架。该框架通过引入注意力机制，有效捕捉不同时间尺度下的特征关联，同时利用长短期记忆网络处理非线性时序特性，克服了传统方法在处理复杂模式时的局限性。实验结果表明，所提方法在多个评价指标上均优于现有主流预测模型，特别是在极端天气条件下的预测稳定性显著提升。此外，通过对模型参数的敏感性分析，进一步验证了其鲁棒性和泛化能力。本研究不仅为季节性电力负荷预测提供了新的思路和技术手段，也为电力系统规划与调度决策支持系统的发展奠定了理论基础。
关键词：季节性电力负荷预测 混合预测框架 深度学习
Abstract
With the continuous expansion of power system scales and the widespread application of renewable energy, accurate seasonal electricity load forecasting plays a crucial role in ensuring stable power system operation, optimizing resource allocation, and promoting energy conservation and emission reduction. To this end, this study aims to develop an efficient optimization method for seasonal electricity load forecasting models to enhance forecasting accuracy and model adaptability. Based on an in-depth analysis of historical electricity load data, combined with multi-source data such as meteorological factors and socioeconomic indicators, a hybrid forecasting fr amework integrating deep learning and traditional statistical methods is proposed. This fr amework effectively captures feature correlations across different time scales by incorporating an attention mechanism and addresses nonlinear time series characteristics using long short-term memory networks, thereby overcoming the limitations of traditional methods in handling complex patterns. Experimental results demonstrate that the proposed method outperforms existing mainstream forecasting models across multiple evaluation metrics, particularly exhibiting significantly improved prediction stability under extreme weather conditions. Furthermore, sensitivity analysis of model parameters further verifies its robustness and generalization capability. This research not only provides new perspectives and technical approaches for seasonal electricity load forecasting but also lays a theoretical foundation for the development of power system planning and dispatch decision support systems.
Keyword:Seasonal Power Load Forecasting Hybrid Forecasting fr amework Deep Learning
目 录
1绪论 1
1.1季节性电力负荷预测的研究背景 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 2
2季节性特征分析与数据预处理 2
2.1季节性电力负荷特征提取 2
2.2数据清洗与异常值处理 3
2.3特征选择与降维方法 3
3模型构建与优化算法 4
3.1基于机器学习的预测模型 4
3.2模型参数优化策略 5
3.3集成学习在负荷预测中的应用 6
4模型评估与实际应用 6
4.1评价指标体系构建 6
4.2实证分析与案例研究 7
4.3应用效果及改进建议 8
结论 8
参考文献 10
致谢 11
