摘要
随着用电结构转变和可再生能源规模化并网导致的电网随机性扰动增加,电网侧需要加强对火电机组等可控可调性发电电源的统筹调度,降低其短期不可控性带来的风险。本文提出小波变换与自回归积分滑动平均(ARIMA)模型相结合的综合法用于火电机组负荷预测。该方法针对火电机组负荷信号的特点,先通过小波变换将历史负荷信号分解为规律性较好的概况信号和随机性较强的细节信号,分别建立ARIMA模型并进行拟合预测,最后加权求和得到最终预测值。利用该方法对某机组正常和异常工况下的负荷运行数据进行预测仿真,仿真结果表明本文方法预测精度明显优于ARIMA方法。
With the increasing random disturbance of power grid caused by changes in electric utility structure and large-scale grid-connection of renewable energy,the grid side needs to strengthen the overall dispatch of controllable and tunable power saurce(like thermal power units)to reduce the short-term uncontrollable risk.An integrated method combining wavelet transform and large-scale autoregressive integrated moving average(ARIMA)model is proposed in this study for load forecasting of thermal power units.Concerning about the characteristics of load signals of thermal power units,firstly,the historical load signals are decomposed into regular signals with good regularity and the detail signals with strong randomness by wavelet transform.Then the ARIMA model is established,fitted and forecast separately.Finally,the final prediction is obtained by weighted summation.The forecasting and simulation of load data of a unit under normal and abnormal conditions shows that,the method in this study has higher precision than the ARIMA model.
作者
张然然
刘鑫屏
ZHANG Ranran;LIU Xinping(School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China)
出处
《热力发电》
CAS
北大核心
2018年第7期52-57,共6页
Thermal Power Generation
基金
国家重点研发计划项目(2017YFB0902102)~~
关键词
火电机组
短期负荷预测
小波变换
ARIMA建模
预测精度
信号分解
拟合预测
thermal power unit
short-term load forecasting
wavelet transform
ARIMA modeling
prediction precision
signal decomposition
fitting and forecast
