Modelling residential house electricity demand profile and analysis of peaksaver program using ANN: case study for Toronto, Canada


Poulad M. E., Fung A. S., He L., ÇOLPAN C. Ö.

INTERNATIONAL JOURNAL OF GLOBAL WARMING, vol.10, pp.158-177, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 10
  • Publication Date: 2016
  • Doi Number: 10.1504/ijgw.2016.077911
  • Journal Name: INTERNATIONAL JOURNAL OF GLOBAL WARMING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.158-177
  • Keywords: demand management, artificial neural network, ANN, greenhouse gas emission, peaksaving, load curtailment activation, LCA, ENERGY-CONSUMPTION
  • Dokuz Eylül University Affiliated: Yes

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.