DocumentCode :
2258650
Title :
Short-term load forecasting in non-residential Buildings
Author :
Penya, Yoseba K. ; Borges, Cruz E. ; Fernández, Iván
Author_Institution :
Univ. of Deusto, Bilbao, Spain
fYear :
2011
fDate :
13-15 Sept. 2011
Firstpage :
1
Lastpage :
6
Abstract :
Short-term load forecasting (STLF) has become an essential tool in the electricity sector. It has been object of vast research since energy load is known to be non-linear and, therefore, very difficult to predict with accuracy. We focus here on non-residential building STLF, an special case of STLF where weather shows smaller influence on the load than in normal scenarios and forecast models, contrary to those on the literature, are required to be simple, avoiding dull and complicated trial-and-error parametrisation or setting-up processes. Under these premises, we have used a two-step methodology comprising a classification and a adjustment steps. Since the non-linearity of the load is associated to the activity in the building, we have demonstrated that the best way to deal with it is using the work day schedule as day-type classifier. Moreover, we have evaluated a number of statistical methods and Artificial Intelligence methods to adjust the typical hourly consumption curve, concluding that an autoregressive time series suffices to fulfil the requirements, even in a 5 day-ahead horizon.
Keywords :
building management systems; load forecasting; statistical analysis; artificial intelligence methods; nonresidential buildings; short-term load forecasting; statistical methods; Artificial neural networks; Bayesian methods; Buildings; Load modeling; Meteorology; Schedules; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
AFRICON, 2011
Conference_Location :
Livingstone
ISSN :
2153-0025
Print_ISBN :
978-1-61284-992-8
Type :
conf
DOI :
10.1109/AFRCON.2011.6072062
Filename :
6072062
Link To Document :
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