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An Optimal Neural Network for Hourly and Daily Energy Consumption Prediction in Buildings

Author

Listed:
  • Fazli Wahid

    (Universiti Tun Hussein Onn, Malaysia)

  • Rozaida Ghazali

    (Universiti Tun Hussein Onn, Malaysia)

  • Lokman Hakim Ismail

    (Universiti Tun Hussein Onn, Malaysia)

  • Ali M. Algarwi Aseere

    (College of Computer Science, King Khalid University, Abha, Saudi Arabia)

Abstract

In this work, hourly and daily energy consumption prediction has been carried out using multi-layer feed forward neural network. The network designed in the proposed architecture has three layers, namely input layer, hidden layer, and output layer. The input layer had eight neurons, output layer had one neuron, and the number of neurons in the hidden layer was varied to find an optimal number for accurate prediction. Different parameters of the neural network were varied repeatedly, and the prediction accuracy was observed for each combination of different parameters to find an optimized combination of different parameters. For hourly energy consumption prediction, a total of six weeks data (September 1 to October 12, 2004) of 10 residential buildings has been used whereas for daily energy consumption prediction, a total of 52 weeks data (January 2004 to December 2004) of 30 residential buildings has been used. To evaluate the performance of the proposed approach, different performance evaluation measurements were applied.

Suggested Citation

  • Fazli Wahid & Rozaida Ghazali & Lokman Hakim Ismail & Ali M. Algarwi Aseere, 2023. "An Optimal Neural Network for Hourly and Daily Energy Consumption Prediction in Buildings," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 14(1), pages 1-13, January.
    • Handle: RePEc:igg:jsir00:v:14:y:2023:i:1:p:1-13
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    References listed on IDEAS

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