Load forecasting using statistical time series model in a medium voltage distribution network

被引：0

作者：

Matsila, Hulisani ^{[1
]}

Bokoro, Pitshou ^{[1
]}

机构：

[1] Univ Johannesburg, Dept Elect & Elect Engn Technol, Johannesburg, South Africa

来源：

IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY | 2018年

关键词：

Time series analysis; auto-regressive integral moving average; load forecasting; mean absolute percentage error;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, the suitability of statistical time series technique in the forecasting of load demand of a public hospital facility is tested. The hospital facility is supplied from a medium voltage distribution network. Historical data recorded over a period of three months are used for this purpose. The R-Studio package software is sourced to examine the shape of the time series pattern. The Box-Jenkins seasonal ARIMA model is subsequently applied in an attempt to forecast future series data and thereby predicting load demand pattern likely to be expected by the hospital facility. The suitability of this prediction technique is verified on the basis of the MAPE. Results show MAPE deviation of 3.91% from actual load data measured.

引用

页码：4974 / 4979

页数：6

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