Research Article
Prediction of Electricity Consumption for Residential Houses in New Zealand
@INPROCEEDINGS{10.1007/978-3-319-94965-9_17, author={Aziz Ahmad and Timothy Anderson and Saeed Rehman}, title={Prediction of Electricity Consumption for Residential Houses in New Zealand}, proceedings={Smart Grid and Innovative Frontiers in Telecommunications. Third International Conference, SmartGIFT 2018, Auckland, New Zealand, April 23-24, 2018, Proceedings}, proceedings_a={SMARTGIFT}, year={2018}, month={7}, keywords={Electricity demand prediction Load prediction Neural network Load management}, doi={10.1007/978-3-319-94965-9_17} }- Aziz Ahmad
Timothy Anderson
Saeed Rehman
Year: 2018
Prediction of Electricity Consumption for Residential Houses in New Zealand
SMARTGIFT
Springer
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 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.
