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Half Hourly Electricity Load Forecasting Using Convolutional Neural Network

  • Abdul Basit Majeed Khan
  • Sajjad Khan
  • Sayeda Aimal
  • Muddassar Khan
  • Bibi Ruqia
  • Nadeem JavaidEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 994)

Abstract

In this paper, enhanced Deep Learning (DL) method is implemented to resolve the accurate electricity load forecasting problem. Electricity load is a factor which plays major role in operations of Smart Grid (SM). For solving this problem, we propose a model which is based on preprocessing, selection and classification of historical data. Features are selected by Combine Feature Selection (CFS) using Decision Tree (DT) and Mutual Information (MI) techniques, and then CFS Convolutional Neural Network (CFSCNN) is used for forecasting of load. Our proposed scheme is also compared with other benchmark schemes. Simulation results show better efficiency and accuracy of proposed model for half hourly electricity load forecasting for one day, one week and one month ahead for the data obtained from ISO NE-CA electricity market.

Keywords

Deep learning Classification Mutual information Smart grid Decision tree Convolutional neural network 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Abdul Basit Majeed Khan
    • 1
  • Sajjad Khan
    • 2
  • Sayeda Aimal
    • 2
  • Muddassar Khan
    • 1
  • Bibi Ruqia
    • 3
  • Nadeem Javaid
    • 2
    Email author
  1. 1.Abasyn University IslamabadIslamabadPakistan
  2. 2.COMSATS University IslamabadIslamabadPakistan
  3. 3.Sardar Bhadur Khan Women University QuettaQuettaPakistan

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