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

Autor:	Mohammad Yusri Hassan, M. S. Majid, Ahmad Sukri Ahmad
Rok vydání:	2012
Předmět:	Building management system Engineering Event (computing) business.industry Group method of data handling Load forecasting Building energy Energy consumption computer.software_genre Machine learning Support vector machine Data mining Artificial intelligence business computer
Zdroj:	2012 IEEE International Conference on Power and Energy (PECon).
DOI:	10.1109/pecon.2012.6450193
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d54c7b6d7b54638989eb212734aa3acf https://doi.org/10.1109/pecon.2012.6450193 Zobrazit plný text záznamu