Hourly Electricity Load Forecasting in Smart Grid Using Deep Learning Techniques

  • Abdul Basit Majeed Khan
  • Nadeem JavaidEmail author
  • Orooj Nazeer
  • Maheen Zahid
  • Mariam Akbar
  • Majid Hameed Khan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 994)


In this paper, a Deep Learning (DL) technique is introduced to forecast the electricity load accurately. We are facing energy shortage in today’s world. So, it is the need of the hour that proper scenario should be introduced to overcome this issue. For this purpose, moving towards Smart Grids (SG) from Traditional Grids (TG) is required. Electricity load is a factor which plays a major role in forecasting. For this purpose, we proposed a model which is based on selection, extraction and classification of historical data. Grey Correlation based Random Forest (RF) and Mutual Information (MI) is performed for feature selection, Kernel Principle Component Analysis (KPCA) is used for feature extraction and enhanced Convolutional Neural Network (CNN) is used for classification. Our proposed scheme is then compared with other benchmark schemes. Simulation results proved the efficiency and accuracy of the proposed model for hourly load forecasting of one day, one week and one month.


Deep learning Smart grid Random forest Mutual Information 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Abdul Basit Majeed Khan
    • 1
  • Nadeem Javaid
    • 2
    Email author
  • Orooj Nazeer
    • 1
  • Maheen Zahid
    • 2
  • Mariam Akbar
    • 2
  • Majid Hameed Khan
    • 3
  1. 1.Abasyn University Islamabad CampusIslamabadPakistan
  2. 2.COMSATS University IslamabadIslamabadPakistan
  3. 3.Group 3 Technology LimitedAldridgeUK

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