NDLI: Customer short term load forecasting by using ARIMA transfer function model

Content Provider	IEEE Xplore Digital Library
Author	Cho, M.Y. Hwang, J.C. Chen, C.S.
Copyright Year	1995
Description	Author affiliation: Dept. of Electr. Eng., Nat. Kaohsiung Inst. of Technol., Taiwan (Cho, M.Y.; Hwang, J.C.)
Abstract	Short-term load forecasting plays an important role in electric power system operation and planning. An accurate load forecasting not only reduces the generation cost in a power system, but also provides a good principle of effective operation. In this paper, the ARIMA model and transfer function model are applied to the short-term load forecasting by considering weather-load relationship. For four types of customer in Taiwan power (Taipower) system, residential load, commercial load, office load and industrial load customers, the summer ARIMA model transfer function model have been derived to proceed the short-term load forecasting during one week. To demonstrate the effectiveness of the proposed method, this paper compares the results of the transfer function model and the univariate ARIMA model with conventional regression. Besides, the transfer function model's accuracy of the load forecast on weekday and weekend is thoroughly investigated. To improve the accuracy level of load forecast, the temperature effect is considered in the transfer function. According to the short-term load forecasting for these four customer classes, it is concluded that the proposed method can achieve better accuracy of load forecast than the ARIMA model by considering the causality between power consumption and temperature.
Starting Page	317
Ending Page	322
File Size	605850
Page Count	6
File Format	PDF
ISBN	0780329813
DOI	10.1109/EMPD.1995.500746
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-11-21
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load forecasting Power system modeling Predictive models Transfer functions Power system planning Temperature Power generation Costs Industrial power systems Industrial relations
Content Type	Text
Resource Type	Article

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