Short Term Load Forecasting based on SV Model

摘要：

The volatility of load time series is noteworthy in load forecating analysis. Considering the characteristic of time-varying variance,a feasible method of short term load forecasting based on Stochastic Volatility (SV) models is presented. The Quasi Maximum Likelihood Estimate(QMLE) is brought in to specify the standard SV model. The model is transformed into state space form,and the Kalman filter is employed to estimate the parameter.Following different conditional distribution,the extended non-Gaussian SV model is proposed. Furthermore,the curve of dynamic volatility is illustrated and the time-varying characteristics in the volatility of load time series is analyzed.Based on the actual daily load data set of Nanjing,the SV type models are specified and daily forecasting is demonstrated,the forecast performance of SV model is compared with GARCH model by three summary index. The empirical results verifies the validity and feasibility of the proposed method.

关键词： Digamma Function Fat-tail Kalman Filter Load Forecasting Quasi Maximum Likelihood Estimate State Space Stochastic Volatility Model

作者: Hao Chen Zhao Zhang Shan Gao Yurong Wang

作者单位: Jiangsu Nanjing Power Supply Company,Nanjing 210008,China Shanghai Municipal Electric Power Company (SMEPC),Shanghai 200122,China School of Electrical Engineering,Southeast University,Nanjing,210096,China School of Electrical Engineering,Southeast University,Nanjing,210096,China;the University of Tenness

会议类型: 国际会议

会议名称: 2010年中国国际供电会议(CICED 2010)

会议地点: 南京

会议语种:英文

页码: 383-388

在线出版日期: 2010-09-13（万方平台首次上网日期，不代表论文的发表时间）