Short term load forecasting using regression trees: Random forest, bagging and M5P

Kumar Srivastava, A.; Singh, D.; Pandey, A.S.

dc.contributor.author	Kumar Srivastava, A.
dc.contributor.author	Singh, D.
dc.contributor.author	Pandey, A.S.
dc.date.accessioned	2020-10-26T06:50:11Z
dc.date.available	2020-10-26T06:50:11Z
dc.date.issued	2020-03
dc.identifier.issn	2278-3091
dc.identifier.uri	http://localhost:8080/xmlui/handle/123456789/849
dc.description.abstract	Decision making in the energy market has to be based on accurate forecasts of the load demand. Therefore, Short Term Load Forecasting (STLF) is important tools in the energy market. In this paper, load forecasting using regression tree methods (Random Forest, Bagging and M5P) are used to effectively forecast the load. The usefulness of the proposed methods has been authenticated through extensive tests using real load data from the Australian electricity market. A comparison of these methods shows that there is an edge in M5P in relation to accuracy. © 2020, World Academy of Research in Science and Engineering. All rights reserved.	en_US
dc.language.iso	en_US	en_US
dc.publisher	World Academy of Research in Science and Engineering	en_US
dc.relation.ispartofseries	International Journal of Advanced Trends in Computer Science and Engineering;Vol. 9 Issue 2
dc.subject	Bagging	en_US
dc.subject	Data mining	en_US
dc.subject	Load Forecasting	en_US
dc.subject	M5P	en_US
dc.subject	Random Forest	en_US
dc.subject	Regression Tree	en_US
dc.title	Short term load forecasting using regression trees: Random forest, bagging and M5P	en_US
dc.type	Article	en_US