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Prediction of Building Energy Consumption Using Enhance Convolutional Neural Network

  • Hafiz Muhammad Faisal
  • Nadeem JavaidEmail author
  • Bakhtawar Sarfraz
  • Abdul Baqi
  • Muhammad Bilal
  • Inzamam Haider
  • Sahibzada Muhammad Shuja
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)

Abstract

Electricity load forecasting plays a vital role in improving the usage of energy through customers to make decisions efficiently. The accuracy of load prediction is a challenging task because of randomness and noise disturbance. An extreme deep learning model is applied in proposed system model to achieve better load prediction accuracy. The proposed model used to extract features by combining the mutual information (RF) and recursive feature elimination (RFE). Furthermore, extreme learning machine (ELM) and enhance CNN are used for load forecasting based on extracted features from MI and RFE. Additionally, to check the performance of our proposed scheme, we compared it with some benchmark schemes e.g. CNN, SVR and MLR. Simulation results reveal that our proposed approach outperformed in prediction performance.

Keywords

Load forecasting Deep learning model Mutual information and recursive feature elimination 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hafiz Muhammad Faisal
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Bakhtawar Sarfraz
    • 2
  • Abdul Baqi
    • 2
  • Muhammad Bilal
    • 2
  • Inzamam Haider
    • 2
  • Sahibzada Muhammad Shuja
    • 1
  1. 1.Comsats University IslamabadIslamabadPakistan
  2. 2.NCBA&EMultanPakistan

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