Application of hybrid GMDH and Least Square Support Vector Machine in energy consumption forecasting

Author

bin Ahmad, Ahmad Sukri ; bin Hassan, M.Y. ; bin Majid, M.S.

Author_Institution

Center of Electr. Energy Syst., Univ. Teknol. Malaysia (UTM), Skudai, Malaysia

fYear

2012

fDate

2-5 Dec. 2012

Firstpage

139

Lastpage

144

Abstract

Forecasting is a tool to predict the future event with the uncertainty and depending on the historical data. It is important for an upcoming planning event because the forecasting result will deliver the initial view for the future. This paper reviews the Least Square Support Vector Machine (LSSVM) and Group Method of Data Handling (GMDH) used in different application of forecasting. Besides, this paper will highlight the possibility of implementing the hybrid GMDH and LSSVM to achieve better accuracy of building energy consumption forecasting.

Keywords

building management systems; data handling; energy consumption; least squares approximations; load forecasting; power engineering computing; support vector machines; LSSVM; building energy consumption forecasting; energy consumption forecasting; group method of data handling approach; hybrid GMDH approach; least square support vector machine; planning event; Buildings; Data mining; Energy consumption; Forecasting; Predictive models; Support vector machines; Time series analysis; Forecasting; GMDH; Hybrid; LSSVM;

fLanguage

English

Publisher

ieee

Conference_Titel

Power and Energy (PECon), 2012 IEEE International Conference on

Conference_Location

Kota Kinabalu

Print_ISBN

978-1-4673-5017-4

Type

conf

DOI

10.1109/PECon.2012.6450193

Filename

6450193