摘 要
随着能源结构转型和智能电网技术的发展，电力系统负荷预测作为保障电网安全稳定运行和优化资源配置的关键环节，其精度和效率要求日益提高。本研究旨在构建一种基于人工智能的电力系统负荷预测模型，以应对传统方法在处理复杂非线性关系时的局限性。通过结合深度学习算法与时间序列分析技术，提出了一种融合长短期记忆网络（LSTM）和注意力机制（Attention Mechanism）的混合预测模型。该模型能够有效捕捉负荷数据中的长期依赖性和关键特征，并通过引入气象、经济等多源异构数据进一步提升预测性能。实验结果表明，所提出的模型在多种场景下的预测误差显著低于传统方法，平均绝对百分比误差（MAPE）降低约20%。此外，模型具备较强的泛化能力和实时适应性，可为电力调度和需求侧管理提供可靠依据。本研究的主要创新点在于将注意力机制与LSTM相结合，实现了对重要特征的动态加权，同时提出了多源数据融合策略以增强模型鲁棒性，为未来智能化电力系统负荷预测提供了新的思路和技术支持。
关键词：电力系统负荷预测；长短期记忆网络（LSTM）；注意力机制（Attention Mechanism）；多源数据融合；平均绝对百分比误差（MAPE）
Abstract
With the transformation of energy structures and the development of smart grid technologies, power system load forecasting, as a critical component for ensuring the safe and stable operation of the grid and optimizing resource allocation, is facing increasingly stringent requirements in terms of accuracy and efficiency. This study aims to construct an artificial intelligence-based power system load forecasting model to address the limitations of traditional methods in handling complex nonlinear relationships. By integrating deep learning algorithms with time series analysis techniques, a hybrid prediction model that combines long short-term memory networks (LSTM) and attention mechanisms is proposed. This model effectively captures long-term dependencies and key features in load data and further enhances forecasting performance by incorporating multisource heterogeneous data, such as meteorological and economic information. Experimental results demonstrate that the proposed model achieves significantly lower prediction errors across various scenarios compared to traditional methods, reducing the mean absolute percentage error (MAPE) by approximately 20%. Moreover, the model exhibits strong generalization capabilities and real-time adaptability, providing a reliable basis for power dispatching and demand-side management. The primary innovations of this study lie in the integration of attention mechanisms with LSTM to dynamically weight important features and the proposal of a multisource data fusion strategy to enhance model robustness, offering new insights and technical support for future intelligent power system load forecasting.
Keywords: Power System Load Forecasting；Long Short-Term Memory Network (Lstm)；Attention Mechanism (Attention)；Multi-Source Data Fusion；Mean Absolute Percentage Error (Mape)
目 录
摘 要 I
Abstract II
一、绪论 1
（一）电力系统负荷预测的研究背景与意义 1
（二）国内外研究现状分析 1
（三）本文研究方法与技术路线 1
二、人工智能在负荷预测中的应用基础 2
（一）负荷预测的基本原理与方法 2
（二）人工智能技术概述及其优势 2
（三）数据预处理与特征提取方法 3
（四）模型评估指标体系构建 3
三、基于人工智能的负荷预测模型设计 4
（一）模型架构的选择与优化 4
（二）关键算法的设计与实现 4
（三）数据驱动的模型训练策略 5
（四）不同场景下的模型适应性分析 6
四、实验验证与结果分析 6
（一）实验数据集的选取与处理 6
（二）模型性能对比与评价 7
（三）预测误差来源及改进措施 7
（四）实际应用案例分析 8
结 论 8
致 谢 10
参考文献 11
