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New variables to improve electricity and natural gas consumption forecasting: dynamic degree-days

E.F. Sánchez-Úbeda, A. Berzosa

This paper describes a new family of derived variables to measure the efect of outdoor air temperature in electricity and natural gas consumption. The proposed Dynamic Degree-Days (DDD) are temperature-derived functions allowing the definition and use of new quantitative indexes which can help to explain easily the daily variations of electricity and natural gas consumption due to temperature. The DDD are based on a piecewise-linear model for daily temperature, previously adjusted using historical data. These new degree-days allow improving energy forecasting models as well as better monitoring energy performance. Illustrative results are presented.


Palabras clave: degree-days; demand; forecasting

14th Conferencia de la Asociación Española para la Inteligencia Artificial - CAEPIA’11, Tenerife, Santa Cruz de Tenerife (España). 07 noviembre 2011

Fecha de publicación: noviembre 2011.



Cita:
E.F. Sánchez-Úbeda, A. Berzosa, New variables to improve electricity and natural gas consumption forecasting: dynamic degree-days, 14th Conferencia de la Asociación Española para la Inteligencia Artificial - CAEPIA’11. ISBN: 978-3-642-25273-0, Tenerife, España, 07-11 Noviembre 2011


    Líneas de investigación:
  • *Predicción y Análisis de Datos

IIT-11-189A

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