Modelling residential house electricity demand profile and analysis of peaksaver program using ANN: case study for Toronto, Canada
by M. Ebrahim Poulad; Alan S. Fung; Lei He; Can Ozgur Colpan
International Journal of Global Warming (IJGW), Vol. 10, No. 1/2/3, 2016

Abstract: A technique is proposed and developed to predict the household hourly electricity demand. The developed artificial neural network (ANN) model of residential hourly demand is employed to estimate the potential impacts of load curtailment activation (LCA) on electricity demand on the activation days. Results are separately discussed in two seasons: summer and winter. LCA occurs once per day for no more than four consecutive hours. Electricity demand increases dramatically after peaksaver/LCA is completed on July 6 and August 30 of 2010. Both days show saving if the data are not normalised. Unnormalised load reductions for individual event hours ranged between 0.35 and 0.64 kWh/h or 14% and 24%, respectively.

Online publication date: Thu, 21-Jul-2016

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Global Warming (IJGW):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com