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高效调度的关键环节，随着智能电网的发展以及可再生能源的广泛应用，传统预测方法难以满足日益复杂的电力系统需求。基于人工智能的负荷预测技术凭借其强大的非线性拟合能力、自适应性和泛化能力成为研究热点。本文旨在综述基于人工智能的电力系统负荷预测技术，分析现有研究中的主要模型与算法，包括神经网络、支持向量机、深度学习等，并探讨不同场景下的应用效果。研究表明，融合多种人工智能算法可以有效提高预测精度，同时结合物理机制建模有助于增强模型的解释性。此外，针对数据质量、模型选择、参数优化等问题提出了改进建议。本文创新性地总结了近年来该领域的最新进展，特别是将深度学习与传统时序分析相结合的方法，为未来研究提供了方向。通过对大量文献的梳理发现，尽管已取得显著成果，但在处理极端天气、突发事件等特殊工况方面仍存在不足，需要进一步探索新的理论和技术手段以提升预测鲁棒性和准确性。
关键词：电力系统负荷预测 人工智能 深度学习
Abstract
Electric load forecasting in power systems is a critical component for ensuring the secure, stable operation and economical dispatch of power systems. With the development of smart grids and the widespread application of renewable energy, traditional forecasting methods are increasingly unable to meet the complex demands of modern power systems. Artificial intelligence (AI)-based load forecasting technologies have emerged as a research focus due to their strong nonlinear fitting capabilities, adaptability, and generalization abilities. This paper reviews AI-based electric load forecasting techniques in power systems, analyzing primary models and algorithms in existing studies, including neural networks, support vector machines, and deep learning, while discussing their application effects under different scenarios. Research indicates that integrating multiple AI algorithms can effectively enhance forecasting accuracy, and combining physical mechanism modeling can improve model interpretability. Additionally, this paper addresses issues such as data quality, model selection, and parameter optimization, offering improvement suggestions. Innovatively summarizing recent advancements in this field, especially methods that combine deep learning with traditional time series analysis, this review provides directions for future research. Through an extensive literature review, it is found that although significant achievements have been made, there remain shortcomings in handling special conditions like extreme weather and emergencies, necessitating further exploration of new theoretical and technical approaches to enhance forecasting robustness and accuracy.
Keyword:Power System Load Forecasting Artificial Intelligence Deep Learning
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国内外研究现状综述 1
1.3研究方法与技术路线 1
2人工智能在负荷预测中的应用原理 2
2.1人工智能技术概述 2
2.2负荷预测模型构建方法 3
2.3数据处理与特征提取技术 3
3常用的人工智能负荷预测模型 4
3.1神经网络模型及其优化 4
3.2支持向量机的应用特点 5
3.3深度学习模型的发展趋势 5
4负荷预测技术的实际应用案例 6
4.1典型应用场景分析 6
4.2实际应用效果评估 7
4.3应用中存在的问题及对策 7
结论 8
参考文献 9
致谢 10
