@inproceedings{48fb4f9d21e440448c2a57c4f2dab9c5,
title = "Prediction of Electricity Consumption for Residential Houses in New Zealand",
abstract = "Residential consumer{\textquoteright}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.",
keywords = "Electricity demand prediction, Load management, Load prediction, Neural network",
author = "Aziz Ahmad and Anderson, {Timothy N} and Rehman, {Saeed Ur}",
year = "2018",
month = jul,
day = "7",
doi = "10.1007/978-3-319-94965-9_17",
language = "English",
isbn = "9783319949642",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
pages = "165--172",
editor = "Chong, {Peter Han} and Boon-Chong Seet and Michael Chai and Rehman, {Saeed Ur}",
booktitle = "Smart Grid and Innovative Frontiers in Telecommunications - 3rd International Conference, SmartGIFT 2018, Proceedings",
note = "3rd International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2018 : Smart Grid and Innovative Frontiers in Telecommunications ; Conference date: 23-04-2018 Through 24-04-2018",
}
