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http://dx.doi.org/10.5302/J.ICROS.2006.12.7.631

GMDH Algorithm with Data Weighting Performance and Its Application to Power Demand Forecasting  

Shin Jae-Ho (인천대학교 전자공학과)
Hong Yeon-Chan (인천대학교 전자공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.12, no.7, 2006 , pp. 631-636 More about this Journal
Abstract
In this paper, an algorithm of time series function forecasting using GMDH(group method of data handling) algorithm that gives more weight to the recent data is proposed. Traditional methods of GMDH forecasting gives same weights to the old and recent data, but by the point of view that the recent data is more important than the old data to forecast the future, an algorithm that makes the recent data contribute more to training is proposed for more accurate forecasting. The average error rate of electric power demand forecasting by the traditional GMDH algorithm which does not use data weighting algorithm is 0.9862 %, but as the result of applying the data weighting GMDH algorithm proposed in this paper to electric power forecasting demand the average error rate by the algorithm which uses data weighting algorithm and chooses the best data weighting rate is 0.688 %. Accordingly in forecasting the electric power demand by GMDH the proposed method can acquire the reduced error rate of 30.2 % compared to the traditional method.
Keywords
GMDH algorithm; data weighting; electric power demand forecasting; data weighting rate;
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