Journal
ADVANCES IN INTELLIGENT SYSTEMS
Volume 138, Issue -, Pages 27-+Publisher
SPRINGER-VERLAG BERLIN
Keywords
Group Method of Data Handling (GMDH); short-term load forecasting (STLF); ARIMA
Funding
- Ministry of Education overseas cooperation Chunhui Projects [Z2007-1-62012]
- NSF of Gansu Province in China [ZS031-A25-010-G]
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Daily power load forecasting plays a significant role in electrical power system operation and planning. Therefore, it is necessary to find automatic interrelations of data and select the optimal structure of model. However, obtaining high accuracy by using single model for short-term load forecasting (STLF) is not easy. In this paper, Group Method of Data Handling (GMDH) is applied to forecast electric load demand of New South Wales (NSW) in Australia from January 17, 2009 to January 18, 2009. Compared with outcomes obtained by ARIMA. we demonstrate that GMDH is a better method for STLF.
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