摘 要
智能电网作为现代电力系统的重要发展方向，其核心在于实现电网的智能化、高效化和可持续化。在智能电网中，电力负荷预测作为电网运行管理的关键环节，对于提高电网调度效率、优化资源配置、保障电力供需平衡具有重要意义。本文聚焦于智能电网中的电力负荷预测模型研究，旨在通过深入探讨各种预测模型的原理、特点及应用效果，为智能电网的精准预测和高效管理提供理论支持和实践指导。本文介绍了智能电网背景下电力负荷预测的重要性。电力负荷预测能够提前对未来一段时间内的电力需求进行准确预估，为电网调度、发电计划和电力市场运营提供可靠依据。随着智能电网的发展，对电力负荷预测的精度和实时性提出了更高要求，因此，研究和开发高效的电力负荷预测模型显得尤为重要。本文详细分析了智能电网中常用的电力负荷预测模型。这些模型主要包括基于时间序列分析的预测模型、基于人工神经网络的预测模型以及基于机器学习算法的预测模型等。每种模型都有其独特的预测原理和适用场景，如时间序列分析模型利用历史负荷数据中的时间相关性进行预测，适用于负荷变化较为平稳的场景；人工神经网络模型通过模拟人脑神经元的工作方式，对非线性负荷数据进行建模和预测，具有较高的预测精度和泛化能力；机器学习算法则通过训练大量历史数据来构建预测模型，能够自动提取特征并优化预测结果。
关键词：智能电网 电力负荷预测 预测模型
Abstract
As an important development direction of modern power system, the core of smart grid is to realize the intelligence, high efficiency and sustainability of power grid. In smart grid, power load forecasting, as a key link of power grid operation management, is of great significance to improve the efficiency of power grid dispatching, optimize resource allocation and ensure the balance of power supply and demand. This paper focuses on the research of power load forecasting models in smart grid, aiming to provide theoretical support and practical guidance for accurate forecasting and efficient management of smart grid by deeply exploring the principles, characteristics and application effects of various forecasting models. This paper introduces the importance of power load forecasting under the background of smart grid. Power load forecasting can accurately predict the power demand in the future period in advance, and provide a reliable basis for grid dispatching, generation planning and power market operation. With the development of smart grid, higher requirements are put forward for the accuracy and real-time performance of power load forecasting, so it is particularly important to research and develop efficient power load forecasting models. This paper analyzes in detail the power load forecasting models commonly used in smart grid. These models mainly include the prediction model based on time series analysis, the prediction model based on artificial neural network and the prediction model based on machine learning algorithm. Each model has its own unique forecasting principle and applicable scenarios. For example, the time series analysis model makes use of the time correlation in the historical load data to forecast, and is suitable for scenarios with relatively stable load changes. By simulating the working mode of human brain neurons, artificial neural network model can model and predict nonlinear load data, which has high prediction accuracy and generalization ability. Machine learning algorithms build predictive models by training large amounts of historical data to automatically extract features and optimize predictive results.
Keyword:Smart grid Power load forecasting Prediction model
目 录
1引言 1
2智能电网与电力负荷预测技术概述 1
2.1智能电网的基本特征 1
2.2电力负荷预测的技术现状 2
2.3负荷预测的评价指标与方法 2
3电力负荷数据预处理与特征提取 2
3.1数据预处理方法 2
3.2负荷数据的特征提取 3
3.3数据质量对预测模型的影响 3
3.4数据处理的科学性与有效性分析 4
4电力负荷预测模型的构建 5
4.1预测模型的选型 5
4.2模型参数优化 5
4.3模型训练与验证 6
4.4模型构建的先进性与实用性分析 6
5模型应用与结果分析 7
5.1案例研究与模型应用 7
5.2预测结果分析与讨论 7
5.3模型应用的局限性与改进建议 8
5.4应用分析的客观性与深入性分析 8
6结论 9
参考文献 10
致谢 11
