- -

New artificial neural network prediction method for electrical consumption forecasting based on building end-uses

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

New artificial neural network prediction method for electrical consumption forecasting based on building end-uses

Show full item record

Escrivá-Escrivá, G.; Álvarez Bel, CM.; Roldán Blay, C.; Alcázar-Ortega, M. (2011). New artificial neural network prediction method for electrical consumption forecasting based on building end-uses. Energy and Buildings. 43(11):3112-3119. doi:10.1016/j.enbuild.2011.08.008

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/45980

Files in this item

Item Metadata

Title: New artificial neural network prediction method for electrical consumption forecasting based on building end-uses
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería Eléctrica - Departament d'Enginyeria Elèctrica
Universitat Politècnica de València. Instituto de Ingeniería Energética - Institut d'Enginyeria Energètica
Issued date:
Abstract:
Due to the current high energy prices it is essential to find ways to take advantage of new energy resources and enable consumers to better understand their load curve. This understanding will help to improve customer ...[+]
Subjects: Artificial neural networks , Building end-uses , Building energy consumption , Forecast method , Active energy , Artificial Neural Network , Commercial customers , Customer flexibility , Demand response programs , Electrical consumption , Electricity market , End-uses , Fundamental features , High energy prices , In-buildings , Load curves , Short term prediction , Total power consumption , Training data sets , Customer satisfaction , Electric load forecasting , Energy resources , Energy utilization , Forecasting , Sales , Neural networks
Copyrigths: Reserva de todos los derechos
Source:
Energy and Buildings. (issn: 0378-7788 )
DOI: 10.1016/j.enbuild.2011.08.008
Publisher:
Elsevier
Publisher version: http://dx.doi.org/10.1016/j.enbuild.2011.08.008
Thanks:
This research work has been possible with the support of the Universitat Politecnica de Valencia (Spain) with grant #CE 19990032.
Type: Artículo

This item appears in the following Collection(s)

Show full item record