TY - GEN
T1 - Application of GMDH to short-term load forecasting
AU - Xu, Hongya
AU - Dong, Yao
AU - Wu, Jie
AU - Zhao, Weigang
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - ARIMA
KW - Group Method of Data Handling (GMDH)
KW - short-term load forecasting (STLF)
UR - https://www.scopus.com/pages/publications/84863359266
U2 - 10.1007/978-3-642-27869-3_4
DO - 10.1007/978-3-642-27869-3_4
M3 - Conference contribution
AN - SCOPUS:84863359266
SN - 9783642278686
T3 - Advances in Intelligent and Soft Computing
SP - 27
EP - 32
BT - Advances in Intelligent Systems - Selected Papers from 2012 International Conference on Control Systems, ICCS 2012
T2 - 2012 International Conference on Environment Science, ICES 2012 and 2012 International Conference on Computer Science, ICCS 2012
Y2 - 15 March 2012 through 16 March 2012
ER -