Prediction of electricity consumption for residential houses in New Zealand
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Authors
Ahmad, Aziz
Anderson, T.N.
Rehman, Saeed
Anderson, T.N.
Rehman, Saeed
Author ORCID Profiles (clickable)
Degree
Grantor
Date
2018-07-10
Supervisors
Type
Conference Contribution - Paper in Published Proceedings
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
Auckland (N.Z.)
New Zealand
residential housing
power consumption
load prediction
neural networks
Levenberg-Marquardt
electricity demand prediction
load management
New Zealand
residential housing
power consumption
load prediction
neural networks
Levenberg-Marquardt
electricity demand prediction
load management
ANZSRC Field of Research Code (2020)
Citation
Ahmad, A., Anderson, T., & Rehman, S. (2018). Prediction of Electricity Consumption for Residential Houses in New Zealand. EAI Endorsed Transactions on Energy Web and Information Technologies, 2018, 165-172. doi:10.1007/978-3-319-94965-9_17
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 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 hours 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.
Publisher
European Alliance for Innovation (EAI)
Permanent link
Link to ePress publication
DOI
doi:10.1007/978-3-319-94965-9_17
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