Prediction of Electricity Consumption for Residential Houses in New Zealand

Aziz Ahmad, Timothy N Anderson, Saeed Ur Rehman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


Residential consumer’s demand of electricity is continuously growing, which leads to high greenhouse gas emissions. Detailed analysis of electricity consumption characteristics for residential buildings is needed to improve efficiency, availability and to plan in advance for periods of high electricity demand. In this research work, we have proposed an artificial neural network based model, which predicts the energy consumption of a residential house in Auckland 24 h in advance with more accuracy than the benchmark persistence approach. The effects of five weather variables on energy consumption was analyzed. Further, the model was experimented with three different training algorithms, the levenberg-marquadt (LM), bayesian regularization and scaled conjugate gradient and their effect on prediction accuracy was analyzed.

Original languageEnglish
Title of host publicationSmart Grid and Innovative Frontiers in Telecommunications - 3rd International Conference, SmartGIFT 2018, Proceedings
EditorsPeter Han Chong, Boon-Chong Seet, Michael Chai, Saeed Ur Rehman
Place of PublicationCham, Switzerland
Number of pages8
ISBN (Electronic)9783319949659
Publication statusPublished - 7 Jul 2018

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
ISSN (Print)1867-8211


  • Electricity demand prediction
  • Load management
  • Load prediction
  • Neural network

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