摘 要
电力系统负荷预测是保障电力系统安全稳定运行和经济调度的重要环节，随着电力市场化改革的推进及可再生能源接入规模的扩大，传统预测方法难以满足日益复杂的负荷特性需求。为此，本文构建基于神经网络的电力系统负荷预测模型，旨在提高预测精度与可靠性。该研究采用深度学习框架下的长短期记忆网络（LSTM），针对电力负荷时间序列数据非线性、强随机性的特点，设计了多层网络结构并引入注意力机制以增强模型对关键特征的捕捉能力。通过对比分析不同算法在相同数据集上的表现，实验结果表明所提模型具有更高的预测精度，在测试集上的平均绝对百分比误差（MAPE）较传统BP神经网络降低了约20%，且能有效处理长期依赖问题。此外，本研究还结合实际电网运行数据进行了验证，证明该模型能够适应多种工况变化，为电力系统规划、调度提供有力支持，其创新之处在于将注意力机制融入LSTM网络中优化电力负荷预测，为后续相关研究提供了新的思路与方法借鉴。
关键词：电力系统负荷预测 长短期记忆网络（LSTM） 注意力机制
Abstract
Electric system load forecasting is a critical component for ensuring the safe, stable operation and economic dispatch of power systems. As the advancement of electricity market reforms and the expansion of renewable energy integration increase the complexity of load characteristics, traditional forecasting methods struggle to meet these evolving demands. To address this challenge, this study develops a neural network-based load forecasting model for power systems, aiming to enhance prediction accuracy and reliability. Utilizing Long Short-Term Memory (LSTM) networks within a deep learning fr amework, the research designs a multi-layered network structure specifically tailored to handle the nonlinear and highly stochastic nature of electric load time series data. An attention mechanism is incorporated to strengthen the model's ability to capture key features. Comparative analysis of different algorithms on the same dataset demonstrates that the proposed model achieves higher prediction accuracy, reducing the Mean Absolute Percentage Error (MAPE) on the test set by approximately 20% compared to traditional Back Propagation (BP) neural networks, while effectively addressing long-term dependency issues. Furthermore, validation using actual grid operation data confirms the model's adaptability to various operating conditions, providing robust support for power system planning and scheduling. The innovation of this study lies in integrating an attention mechanism into LSTM networks to optimize electric load forecasting, offering new insights and methodological references for future research in this field.
Keyword:Electricity System Load Forecasting Long Short-Term Memory Network (Lstm) Attention Mechanism
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神经网络模型构建 2
2.1神经网络基础理论 2
2.2模型架构设计 3
2.3参数优化方法 3
3数据处理与特征提取 4
3.1数据预处理技术 4
3.2特征选择方法 5
3.3异常数据处理 5
4负荷预测模型应用 6
4.1模型训练与验证 6
4.2预测结果分析 7
4.3实际案例应用 7
结论 8
参考文献 9
致谢 10
