An association-rule based method for short-term electricity demand forecasting and consumption pattern recognition

Investigador: Miguel Ángel Zúñiga García
Disciplina: Ingeniería y tecnología/Computación y Ciencias de la Información  
Tipo de contenido: Ponencia

Resumen:

Electricity is a commodity that cannot be stored efficiently. Electricity has to be produced at the same time is needed. Electricity demand is highly related to human behaviour. Human behaviour is a rutinary process (Almost every day we eat at the same time, go to work at the same time, go to sleep at the same time, etc.). Association rules is a suitable method to model this kind of behavior. In this work, Association rules method is implemented to analyze 2 year of load data to get especific rules at different moments in a day. The main result is a projection of the most and leas predictable moments in the day to forecast electricity consumption.

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