Electricity Load Forecasting for Each Day of Week Using Deep CNN

  • Sajjad Khan
  • Nadeem JavaidEmail author
  • Annas Chand
  • Abdul Basit Majeed Khan
  • Fahad Rashid
  • Imran Uddin Afridi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)


In smart grid, precise and accurate electricity load forecasting is one of the most challenging tasks. It is due to the high volatile, non-stationary and non-linear behavior of electricity load data. In this paper, a Deep Convolution Neural Network (DCNN) model is proposed to forecast the electricity load for each day of the week of Victoria (Australia). To forecast the electricity load for one day of the week, we analyzed the electricity load data consumed on the same day for the previous three months. To show the usefulness of our proposed scheme, comparison is made with the state of the art forecasting models namely recurrent neural network, extreme learning machine, CNN and auto regressive integrated moving average. Results show that the proposed DCNN has the lowest mean absolute percentage error, mean absolute error and root mean square error of 2.1%, 138.771 and 116.417.


Smart grid Forecasting Energy management Neural Network Deep learning 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sajjad Khan
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Annas Chand
    • 2
  • Abdul Basit Majeed Khan
    • 3
  • Fahad Rashid
    • 4
  • Imran Uddin Afridi
    • 1
  1. 1.COMSATS University IslamabadIslamabadPakistan
  2. 2.COMSATS University Islamabad, Abbottabad CampusAbbottabadPakistan
  3. 3.Abasyin University IslamabadIslamabadPakistan
  4. 4.Bahria University IslamabadIslamabadPakistan

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