摘要
为支撑电力现货市场下实时交易与安全校核的需要,提出了一种基于分类波动性统计的短期负荷区间预测方法。首先,介绍了传统的负荷波动性统计分析及区间预测限值计算方法,通过将负荷历史数据标幺化处理,绘制负荷波动性分布直方图,计算在给定精度下的区间限值;接着结合我国实际,从居民、商业、工业三类用户出发,分别讨论不同类型用户负荷的波动性特点,在此基础上汇总形成全网负荷区间预测的上、下限值,实现对全网负荷的区间预测。最后基于某省实际数据构造的算例表明,通过深入分析不同类型负荷的波动性,本文所提出的方法能实现预测准确性和区间宽度的整体最优,在保证相同的预测精度的前提下,减少区间宽度,提高负荷区间预测结果的实用性。
In order to support the demand of real-time transaction and safety check in the spot electricity market,a short-term load interval prediction method based on classified volatility statistics is proposed.Firstly,the traditional statistical analysis of load volatility and the calculation method of interval prediction limit are introduced.By standardizating the historical data of load,the distribution histogram of load volatility is drawn,and the interval limit under the given precision is calculated.Then,based on the reality of China,this paper discusses the fluctuation characteristics of load of different types of users from the perspectives of residents,commercial and industrial users,and summarizes the upper and lower limits of load interval prediction of the whole network,so as to realize the interval prediction of the whole network load.Finally,a case study constructed based on the actual data of a certain province shows that through in-depth analysis of the fluctuation of different types of loads,the method proposed in this paper can achieve the overall optimal prediction accuracy and interval width,reduce the interval width and improve the practicability of load interval prediction results on the premise of ensuring the same prediction accuracy.
作者
吴茵
莫东
李秋文
张德亮
黄红伟
毛文照
WU Yin;MO Dong;LI Qiuwen;ZHANG Deliang;HUANG Hongwei;MAO Wenzhao(Guangxi Power Grid Power Dispatching Center,Nanning 530000 Guangxi,China;Beijing Qingda Keyue Co.,Ltd.,Beijing 100084,China)
出处
《电力大数据》
2019年第11期14-20,共7页
Power Systems and Big Data
基金
南方电网公司科技项目(考虑多市场化交易的长中短期发电调度优化和安全校核技术研究及应用GXKJXM20170362)支持
关键词
电力现货市场
短期负荷预测
区间预测
波动性统计
预测准确率
power spot market
short-term load forecasting
interval prediction
volatility statistics
prediction accuracy
