摘要随着我国经济水平的提高，用户对电力负荷的需求越来越大，国家电力系统所面临的挑战也越来越大[1]。提高电力负荷预测水平，有利于用电的合理与经济，有利于节约煤、油等发电原料，同时也可以节约成本减少污染，保护环境，对电网建设规划，电力系统经济效益和社会效益的提高有很大的作用。电力系统的安全性、可靠性、经济性是评价一个系统的重要指标，短期负荷预测作为实现电力系统优化运行的基础，对系统的性能改善有很大的作用。当前，经济增长和人们生活水平的不断提升促使国家电网的结构发生变化，使其向着智能电网的方向发展，这使得负荷预测的要求越来越高，因此，短期负荷预测的精度与稳定性需要更高，智能算法的重要性日益体现。 71600
本文对负荷预测的原理进行了详细的阐述，对智能算法进行分析总结，利用智能算法对电力系统负荷进行预测。本文以最小二乘支持向量机为模型，运用粒子群算法优化预测模型的参数，使得模型更加准确，减小误差，提高预测结果的可靠性。
该论文有图6幅，表6个，参考文献21篇。
毕业论文关键词：电力系统负荷预测 粒子群算法 最小二乘支持向量机
Power system load forecasting based on Intelligent Algorithm
Abstract With the improvement of the economy in our country, the demand of the users for electric load is bigger and bigger, the challenge of the national electric power system is also more and more arduous.To improve the technical level of the load forecasting is conducive to the reasonable and economic use of electric energy.It can also save coal, oil and other raw materials for power generation. At the same time, it can save the cost,reduce the pollution and protect the environment.It has great effect on the power grid planning,economic and social benefit of the power system.The security, reliability and economy of power system are the most important indexes to evaluate a system.Short term load forecasting is the basis of power system optimization, which has a great effect on the performance of the system.The current economic growth and the improvement of people’s living standards lead to the change of the structure of the notional grid, and the development of smart grid.These make the load forecasting demand higher and higher, therefore, the short-term load forecasting’s accuracy and stability requirements need to be better.At this time ,the importance of intelligent algorithms is increasingly reflected.
In this article, the principle of load forecasting is described in detail. The intelligent algorithm is analyzed and summarized, and the power system load is forecasted by the intelligent algorithm. In this article, the Least Squares Support Vector Machine is used as the model.The parameters of the prediction model are optimized by Particle Swarm Optimization Algorithm, which makes the model more accurate, and it can reduce the error, improve the reliability of prediction results.
There are 6 figures, 6 tables,and 21 references in this article.
Key Words: Power system load forecasting Particle swarm optimization (PSO) Least squares support vector machine (LSSVM)
目 录
摘 要 I
Abstract II
图清单 VI
表清单 VI
1 绪 论 1
1.1研究背景及意义 1
1.3本文主要内容 4
2 电力负荷预测研究 智能算法的电力系统负荷预测:http://www.youerw.com/zidonghua/lunwen_81384.html