NDLI: Electricity consumption forecasting in peak load month based on variable weight combination forecasting model

Content Provider	IEEE Xplore Digital Library
Author	Jia Zhengyuan
Copyright Year	2008
Description	Author affiliation: Dept. of Econ. Manage., North China Electr. Power Univ., Baoding (Jia Zhengyuan)
Abstract	According to the good growth characteristics of GM (1, 1), the growing trend of the monthly load time series is simulated with vertical historical load data as samples. According to the characteristics of ARIMA model that can better describe the non-stationary data series, the growing trend of the monthly load is simulated with horizontal historical load data as samples. The variable weight is introduced, and the variable weight combination forecasting model combining the merits of GM (1, 1) and ARIMA model is established, which is then applied to forecast the electricity consumption in the peak load month. Experiment results compared with single forecasting model show that the method has a more stable and less forecasting error.
Starting Page	1265
Ending Page	1269
File Size	136334
Page Count	5
File Format	PDF
ISBN	9781424425020
DOI	10.1109/ICAL.2008.4636346
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Time series analysis forecasting ARIMA Predictive models Data models GM (1, 1) Mathematical model Forecasting Load modeling variable weight combination
Content Type	Text
Resource Type	Article

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