Title :
An intelligent relational pattern matching system for electricity demand prediction
Author :
Jiangxia Zhong ; Xinghuo Yu ; Combariza, Miguel ; Jinjian Wang
Author_Institution :
Sch. of Electr. & Comput. Eng., RMIT Univ., Melbourne, VIC, Australia
Abstract :
The forecast of electricity consumption is a key element to develop successful policies for electricity demand management. A significant variable affecting the demand of electric energy in commercial and industrial building is the outdoor temperature. In this paper, an intelligent relational pattern matching system is proposed to forecast electricity demand using smart meter data and outdoor temperature profiles. In order to identify the relationship map between the patterns of power consumption and temperature, a learning rule based approach is developed to incrementally learn the correlation between both pattern bases. One-week-ahead forecast is executed by a similarity search method using the found relationship map. The effectiveness of the proposed system and prediction method is verified using real smart meter data from a commercial building, and weather forecasts from the Australian Bureau of Meteorology.
Keywords :
building management systems; demand forecasting; demand side management; learning (artificial intelligence); pattern matching; power consumption; power engineering computing; smart meters; weather forecasting; commercial building; electricity consumption forecasting; electricity demand forecasting; electricity demand management prediction method; industrial building; intelligent relational pattern matching system; learning rule based approach; one-week-ahead forecast; outdoor temperature profiles; power consumption; smart meter data; weather forecasts; Electricity; Forecasting; Meteorology; Power demand; Smart meters; Temperature distribution; Temperature measurement; Clustering; Data analysis; Electricity Demand; Forecasting; Pattern matching; Smart meter;
Conference_Titel :
Industrial Electronics Society, IECON 2014 - 40th Annual Conference of the IEEE
DOI :
10.1109/IECON.2014.7049020
