智能用电大数据环境下的短期负荷预测研究

发布时间：2018-07-10 14:09

本文选题：负荷聚类 + 负荷预测　；参考：《华北电力大学(北京)》2017年硕士论文

【摘要】：负荷预测一直以来都是电力系统的一项重要工作,准确的负荷预测可以经济合理地安排电力系统发电机组启停,对于保持电网的安全稳定运行、保持社会的正常生产秩序、有效降低发电成本有着重要作用。随着智能电网技术的发展,高级量测体系和各种监控系统大规模的部署。智能电表是高级量测体系的重要组成部分,能够获得用户一定时间间隔内精确的用电负荷。智能电表产生数据的速度快、体量大,产生的数据没有进行深入分析,造成了数据的浪费。因此充分挖掘用电数据的价值,研究智能用电大数据环境下的短期负荷预测具有重要意义。针对智能电表能获取用户级详细用电数据的特点,本文从少量用户数据入手,首先通过负荷聚类,分析了用户用电行为之间的相似性;在此基础上提出了基于OS-ELM的短期负荷预测模型,并通过仿真实验验证了所提模型能够提升负荷预测精度且进一步展示了聚类结果与预测精度之间的关系;之后为适应智能用电大数据环境,进一步提出了基于Spark的并行OS-ELM短期负荷预测模型,并在实验中验证了模型能在保证预测精度的前提下具有较高的效率。本文具体工作如下:1.研究了用于负荷聚类的日期类型因素。针对不同的日期类型(普通工作日、节假日前一天、节假日)分别计算用户的典型日负荷,把三种典型日负荷曲线拼接起来作为用户的典型负荷曲线;然后对用户典型负荷曲线进行聚类操作,挖掘用户用电行为之间的相似性。2.针对少量用户数据,提出了基于OS-ELM的短期负荷预测模型。在负荷聚类基础上,对不同的用户类分别采用该负荷预测模型进行负荷预测并汇总得到系统级的负荷预测。在MATLAB平台上进行仿真实验,验证了所提模型的有效性,并进一步展示了预测精度随聚类数目变化的关系。3.针对海量用户数据,进一步提出了基于Spark的并行OS-ELM短期负荷预测模型。为了适应智能用电大数据环境,在所提基于OS-ELM的预测模型基础上,提出了基于Spark的并行OS-ELM短期负荷预测模型,并在仿真实验中验证了所提并行预测模型在保证负荷预测精度的前提下仍具有较高的运行效率。
[Abstract]:Load forecasting has always been an important work of the power system. Accurate load forecasting can reasonably arrange power system generator set up and stop, which plays an important role in maintaining the safe and stable operation of the power grid, maintaining the normal production order of the society and reducing the cost of power generation effectively. The intelligent meter is an important part of the advanced measurement system. The intelligent meter is an important part of the advanced measurement system. It can obtain the accurate power load of the user in a certain time interval. The speed of the data is fast and the volume is large. The data produced is not deeply analyzed, and the data is wasted. Therefore, it is fully dug. It is of great significance to study the value of electrical data and to study the short-term load forecasting in the intelligent data environment. In view of the characteristics of the intelligent meter, which can obtain the detailed user level data, this paper begins with a small amount of user data, and first analyzes the similarity between the user's electricity use behavior through a small amount of user data. On this basis, the basis is put forward. In the short term load forecasting model of OS-ELM, the simulation experiments show that the proposed model can improve the load forecasting precision and further demonstrate the relationship between the clustering results and the prediction accuracy. Then, the parallel OS-ELM short-term load forecasting model based on Spark is further proposed to adapt to the intelligent data environment. It is proved that the model can have high efficiency on the premise of guaranteeing the prediction accuracy. The specific work of this paper is as follows: 1. the date type factors for load clustering are studied. For different date types (ordinary working day, holiday day, holiday), the typical daily load of users is calculated respectively, and three typical daily load curves are spliced. As a typical load curve of the user, the user's typical load curve is clustered, and the similarity between the user's electrical behavior is excavated for a small amount of user data, and a short-term load forecasting model based on OS-ELM is proposed. On the basis of load clustering, the load forecasting model is used for the different user classes to carry out the load forecasting model respectively. The load forecast is predicted and summarized. The simulation experiments on the MATLAB platform verify the validity of the proposed model, and further demonstrate the relationship between the prediction accuracy and the number of cluster numbers..3. is further proposed for the parallel OS-ELM short-term load forecasting model based on Spark. On the basis of the prediction model based on OS-ELM, a parallel OS-ELM short-term load forecasting model based on Spark is put forward on the basis of the forecast model based on the electric big data. It is proved that the proposed parallel prediction model still has a high operating efficiency under the premise of guaranteeing the precision of load forecasting.
【学位授予单位】：华北电力大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP311.13;TM715

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