NDLI: Short term load forecasting using regime-switching GARCH models

Content Provider	IEEE Xplore Digital Library
Author	Hao Chen Fangxing Li Qiulan Wan Yurong Wang
Copyright Year	2011
Description	Author affiliation: School of Electrical Engineering, Southeast University, Nanjing, China, 210096 (Qiulan Wan) \|\| Southeast University, Nanjing, China, 210096 (Yurong Wang) \|\| Jiangsu Electric Power Company Nanjing Power Supply Company, Nanjing, China, 210008 (Hao Chen) \|\| Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA (Fangxing Li)
Abstract	Modeling the volatility in load time series can contribute to improving the performance of short-term load forecasting (STLF). In this work, to capture the nonlinear characteristics of volatility, regime switching in the volatility of load time series is investigated. By combining regime-switching models with Generalized Auto-Regressive Conditional Heteroscedastic (GARCH) models, two types of regime-swithcing GARCH models, Threshold Auto-Regressive GARCH (TAR-GARCH) and Logistic Smooth Transition Auto-Regressive GARCH (LSTAR-GARCH) load forecasting models, are studied. In addition, LSTAR is effectively used to handle the discontinuity point problem of TAR near the threshold. Furthermore, the fat-tail effect in load time series is examined, and the regime switching GARCH models with fat-tail distribution are proposed for generalization. Case study on a practical sample for STLF clearly validates the feasibility and effectiveness of the proposed methods. The slope structure of News Impact Curve (NIC) is proposed to depict the behavior of TAR-GARCH and LSTAR-GARCH type models near the threshold. Forecasting results by all the presented regime-switching GARCH type models are provided. It is concluded that LSTAR-GARCH model with fat-tail distribution is a promising method for STLF.
Starting Page	1
Ending Page	6
File Size	1099963
Page Count	6
File Format	PDF
ISBN	9781457710001
ISSN	19449925
e-ISBN	9781457710025
e-ISBN	9781457710018
DOI	10.1109/PES.2011.6039457
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load modeling Mathematical model Predictive models Time series analysis Switches Autoregressive processes Load forecasting News Impact Curve (NIC) Fat Tail GARCH Load Forecasting Logistic Function LSTAR-GARCH Regime Switching TAR-GARCH
Content Type	Text
Resource Type	Article

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