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http://dx.doi.org/10.5370/KIEE.2018.67.10.1257

Development of ARIMA-based Forecasting Algorithms using Meteorological Indices for Seasonal Peak Load  

Jeong, Hyun Cheol (Dept. of Electrical Engineering, Dong-A University)
Jung, Jaesung (Dept. of Energy Systems Research, Ajou University)
Kang, Byung O (Dept. of Electrical Engineering, Dong-A University)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.67, no.10, 2018 , pp. 1257-1264 More about this Journal
Abstract
This paper proposes Autoregressive Integrated Moving Average (ARIMA)-based forecasting algorithms using meteorological indices to predict seasonal peak load. First of all, this paper observes a seasonal pattern of the peak load that appears intensively in winter and summer, and generates ARIMA models to predict the peak load of summer and winter. In addition, this paper also proposes hybrid ARIMA-based models (ARIMA-Hybrid) using a discomfort index and a sensible temperature to enhance the conventional ARIMA model. To verify the proposed algorithm, both ARIMA and ARIMA-Hybrid models are developed based on peak load data obtained from 2006 to 2015 and their forecasting results are compared by using the peak load in 2016. The simulation result indicates that the proposed ARIMA-Hybrid models shows the relatively improved performance than the conventional ARIMA model.
Keywords
Peak load; ARIMA model; Discomfort index; Sensible temperature;
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