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Short-Term Forecasting of Monthly Maximum Electric Power Loads Using a Winters' Multiplicative Seasonal Model  

Yang, Moonhee (Department of Industrial Engineering, Donkook University)
Lim, Sanggyu (Department of Industrial Systems Engineering, Gyeongsang National University)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.28, no.1, 2002 , pp. 63-75 More about this Journal
Abstract
To improve the efficiency of the electric power generation, monthly maximum electric power consumptions for a next one year should be forecasted in advance and used as the fundamental input to the yearly electric power-generating master plan, which has a greatly influence upon relevant sub-plans successively. In this paper, we analyze the past 22-year hourly maximum electric load data available from KEPCO(Korea Electric Power Corporation) and select necessary data from the raw data for our model in order to reflect more recent trends and seasonal components, which hopefully result in a better forecasting model in terms of forecasted errors. After analyzing the selected data, we recommend to KEPCO the Winters' multiplicative model with decomposition and exponential smoothing technique among many candidate forecasting models and provide forecasts for the electric power consumptions and their 95% confidence intervals up to December of 1999. It turns out that the relative errors of our forecasts over the twelve actual load data are ranged between 0.1% and 6.6% and that the average relative error is only 3.3%. These results indicate that our model, which was accepted as the first statistical forecasting model for monthly maximum power consumption, is very suitable to KEPCO.
Keywords
electricity; electric power forecasting; monthly maximum power consumption;
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