摘 要
随着能源结构转型和智能电网技术的快速发展，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核心环节，其重要性日益凸显。传统负荷预测方法在处理复杂非线性关系和大规模数据时存在局限性，而人工智能技术凭借强大的数据挖掘能力和模式识别能力为负荷预测提供了新思路。本研究旨在探索基于人工智能的电力系统负荷预测方法，以提高预测精度和实用性。研究首先分析了多种人工智能算法的特点及其在负荷预测中的适用性，选取深度学习、支持向量机和随机森林等典型算法构建预测模型，并结合实际电力负荷数据进行训练与验证。同时，提出了一种融合多源数据特征提取和自适应参数优化的改进算法，有效解决了传统模型在数据噪声敏感性和动态环境适应性方面的不足。
关键词：电力系统负荷预测 人工智能 深度学习
Abstract
With the transformation of energy structure and the rapid development of smart grid technology, the importance of power system load prediction, as the core link to ensure the safe and stable operation of the power grid and optimize the resource allocation, is becoming increasingly prominent. Traditional load forecasting methods have limitations in dealing with complex non-linear relationships and large-scale data, while artificial intelligence technology provides a new idea for load forecasting with its powerful data mining capabilities and pattern recognition capabilities. This study aims to explore AI-based power system load prediction methods to improve prediction accuracy and utility. Firstly, the characteristics and applicability of a variety of artificial intelligence algorithms in load prediction are analyzed, and typical algorithms such as deep learning, support vector machine and random forest are selected to build the prediction model, and trained and verified by combining the actual power load data. Meanwhile, an improved algorithm integrating feature extraction and adaptive parameter optimization of multi-source data is proposed, which effectively solves the shortcomings of traditional models in data noise sensitivity and dynamic environment adaptability.
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研究背景 1
1.2基于人工智能的负荷预测意义 1
1.3国内外研究现状分析 1
1.4本文研究方法概述 2
2人工智能技术在负荷预测中的应用基础 2
2.1人工智能技术的基本原理 2
2.2常用人工智能算法介绍 3
2.3数据预处理与特征提取方法 3
2.4模型选择与优化策略 4
2.5技术应用中的挑战与机遇 4
3负荷预测模型的设计与实现 4
3.1负荷预测模型的需求分析 5
3.2基于神经网络的预测模型构建 5
3.3基于深度学习的改进模型设计 5
3.4模型训练与验证流程 6
3.5实验结果与性能评估 6
4负荷预测的实际应用与案例分析 7
4.1实际电力系统的数据采集 7
4.2不同场景下的预测效果分析 7
4.3短期与长期负荷预测对比 8
4.4模型鲁棒性与适应性测试 8
4.5应用中存在的问题与改进建议 9
结论 9
参考文献 11
致谢 12
