摘要
随着能源需求的快速增长和可再生能源的广泛接入，电力系统负荷预测的重要性日益凸显。准确的负荷预测能够优化电力调度、降低运行成本并提升系统稳定性。本研究旨在探索人工智能技术在电力系统短期和中长期负荷预测中的应用潜力，并提出一种基于深度学习与改进优化算法的混合预测模型。该模型结合长短期记忆网络（LSTM）的时间序列处理能力与粒子群优化（PSO）算法的参数优化优势，有效提升了预测精度。通过引入气象数据、历史负荷及社会经济因素等多源信息，模型能够适应复杂非线性变化特征。实验结果表明，相较于传统统计方法和单一机器学习模型，所提方法在均方根误差（RMSE）和平均绝对百分比误差（MAPE）指标上分别降低了15%和20%，展现出更高的鲁棒性和泛化能力。此外，研究还设计了一种动态权重调整机制，以应对不同场景下的数据波动问题。这一创新显著增强了模型在极端天气和突发事件条件下的预测性能。综上所述，本研究为人工智能技术在电力系统负荷预测领域的实际应用提供了新的思路和技术支撑，具有重要的理论价值和工程意义。
关键词：电力系统负荷预测；深度学习；粒子群优化
Abstract
With the rapid growth of energy demand and the extensive integration of renewable energy, the importance of load forecasting in power systems has become increasingly prominent. Accurate load forecasting can optimize power dispatching, reduce operational costs, and enhance system stability. This study aims to explore the application potential of artificial intelligence technologies in short-term and medium-to-long-term load forecasting for power systems and proposes a hybrid prediction model based on deep learning and an improved optimization algorithm. The model leverages the time-series processing capability of Long Short-Term Memory (LSTM) networks and the parameter optimization advantages of Particle Swarm Optimization (PSO), effectively improving forecasting accuracy. By incorporating multisource information such as meteorological data, historical loads, and socioeconomic factors, the model is capable of adapting to complex nonlinear change characteristics. Experimental results demonstrate that, compared with traditional statistical methods and single machine learning models, the proposed method reduces the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) by 15% and 20%, respectively, showcasing higher robustness and generalization ability. Additionally, this research designs a dynamic weight adjustment mechanism to address data fluctuation issues under different scenarios. This innovation significantly enhances the model's predictive performance under extreme weather conditions and unexpected events. In summary, this study provides new insights and technical support for the practical application of artificial intelligence technologies in load forecasting for power systems, offering important theoretical value and engineering significance.
Keywords：Power System Load Forecasting; Deep Learning; Particle Swarm Optimization
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 1
(二) 人工智能在负荷预测中的意义 1
(三) 国内外研究现状分析 1
(四) 本文研究方法与内容安排 2
二、人工智能技术在负荷预测中的基础理论 2
(一) 负荷预测的基本概念与分类 2
(二) 人工智能算法的适用性分析 3
(三) 常见人工智能模型在负荷预测中的应用 3
(四) 数据预处理与特征提取的重要性 4
三、人工智能驱动的负荷预测模型构建 5
(一) 模型选择与优化策略 5
(二) 数据驱动的预测方法设计 5
(三) 不同场景下的模型适应性分析 6
(四) 模型性能评估与改进方向 6
四、实际案例分析与效果验证 7
(一) 典型电力系统的数据采集与处理 7
(二) 人工智能模型的实际应用案例 7
(三) 预测结果对比与误差分析 8
(四) 提升预测精度的关键因素探讨 8
结 论 10
参考文献 11
随着能源需求的快速增长和可再生能源的广泛接入，电力系统负荷预测的重要性日益凸显。准确的负荷预测能够优化电力调度、降低运行成本并提升系统稳定性。本研究旨在探索人工智能技术在电力系统短期和中长期负荷预测中的应用潜力，并提出一种基于深度学习与改进优化算法的混合预测模型。该模型结合长短期记忆网络（LSTM）的时间序列处理能力与粒子群优化（PSO）算法的参数优化优势，有效提升了预测精度。通过引入气象数据、历史负荷及社会经济因素等多源信息，模型能够适应复杂非线性变化特征。实验结果表明，相较于传统统计方法和单一机器学习模型，所提方法在均方根误差（RMSE）和平均绝对百分比误差（MAPE）指标上分别降低了15%和20%，展现出更高的鲁棒性和泛化能力。此外，研究还设计了一种动态权重调整机制，以应对不同场景下的数据波动问题。这一创新显著增强了模型在极端天气和突发事件条件下的预测性能。综上所述，本研究为人工智能技术在电力系统负荷预测领域的实际应用提供了新的思路和技术支撑，具有重要的理论价值和工程意义。
关键词：电力系统负荷预测；深度学习；粒子群优化
Abstract
With the rapid growth of energy demand and the extensive integration of renewable energy, the importance of load forecasting in power systems has become increasingly prominent. Accurate load forecasting can optimize power dispatching, reduce operational costs, and enhance system stability. This study aims to explore the application potential of artificial intelligence technologies in short-term and medium-to-long-term load forecasting for power systems and proposes a hybrid prediction model based on deep learning and an improved optimization algorithm. The model leverages the time-series processing capability of Long Short-Term Memory (LSTM) networks and the parameter optimization advantages of Particle Swarm Optimization (PSO), effectively improving forecasting accuracy. By incorporating multisource information such as meteorological data, historical loads, and socioeconomic factors, the model is capable of adapting to complex nonlinear change characteristics. Experimental results demonstrate that, compared with traditional statistical methods and single machine learning models, the proposed method reduces the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) by 15% and 20%, respectively, showcasing higher robustness and generalization ability. Additionally, this research designs a dynamic weight adjustment mechanism to address data fluctuation issues under different scenarios. This innovation significantly enhances the model's predictive performance under extreme weather conditions and unexpected events. In summary, this study provides new insights and technical support for the practical application of artificial intelligence technologies in load forecasting for power systems, offering important theoretical value and engineering significance.
Keywords：Power System Load Forecasting; Deep Learning; Particle Swarm Optimization
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 1
(二) 人工智能在负荷预测中的意义 1
(三) 国内外研究现状分析 1
(四) 本文研究方法与内容安排 2
二、人工智能技术在负荷预测中的基础理论 2
(一) 负荷预测的基本概念与分类 2
(二) 人工智能算法的适用性分析 3
(三) 常见人工智能模型在负荷预测中的应用 3
(四) 数据预处理与特征提取的重要性 4
三、人工智能驱动的负荷预测模型构建 5
(一) 模型选择与优化策略 5
(二) 数据驱动的预测方法设计 5
(三) 不同场景下的模型适应性分析 6
(四) 模型性能评估与改进方向 6
四、实际案例分析与效果验证 7
(一) 典型电力系统的数据采集与处理 7
(二) 人工智能模型的实际应用案例 7
(三) 预测结果对比与误差分析 8
(四) 提升预测精度的关键因素探讨 8
结 论 10
参考文献 11
