Smart Grid and Innovative Frontiers in Telecommunications. Third International Conference, SmartGIFT 2018, Auckland, New Zealand, April 23-24, 2018, Proceedings

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
Aziz Ahmad1,*, Timothy Anderson2, Saeed Rehman2,*
  • 1: Unitec Institute of Technology
  • 2: Auckland University of Technology
*Contact email: aahmad@unitec.ac.nz, saeed.rehman@aut.ac.nz

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.