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 contribution

2 Citations (Scopus)

Abstract

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 publicationInternational Conference on Smart Grid Inspired Future Technologies
EditorsPeter Han Chong, Boon-Chong Seet, Michael Chai, Saeed Ur Rehman
Place of PublicationCham, Switzerland
Pages165-172
Number of pages8
ISBN (Electronic)9783319949659
DOIs
Publication statusPublished - 7 Jul 2018

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume245
ISSN (Print)1867-8211

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  • Cite this

    Ahmad, A., Anderson, T. N., & Rehman, S. U. (2018). Prediction of Electricity Consumption for Residential Houses in New Zealand. In P. H. Chong, B-C. Seet, M. Chai, & S. U. Rehman (Eds.), International Conference on Smart Grid Inspired Future Technologies (pp. 165-172). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 245).. https://doi.org/10.1007/978-3-319-94965-9_17