An intelligent relational pattern matching system for electricity demand prediction
作者： Jiangxia Zhong , Xinghuo Yu , Miguel Combariza , Jinjian Wang
DOI: 10.1109/IECON.2014.7049020
关键词:
摘要: The forecast of electricity consumption is a key element to develop successful policies for demand management. A significant variable affecting the electric energy in commercial and industrial building outdoor temperature. In this paper, an intelligent relational pattern matching system proposed using smart meter data temperature profiles. order identify relationship map between patterns power temperature, learning rule based approach developed incrementally learn correlation both bases. One-week-ahead executed by similarity search method found map. effectiveness prediction verified real from building, weather forecasts Australian Bureau Meteorology.
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