Short Term Load Forecasting Using XGBoost

  • Raza Abid Abbasi
  • Nadeem JavaidEmail author
  • Muhammad Nauman Javid Ghuman
  • Zahoor Ali Khan
  • Shujat Ur Rehman
  • Amanullah
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)


For efficient use of smart grid, exact prediction about the in-future coming load is of great importance to the utility. In this proposed scheme initially we converted daily Australian energy market operator load data to weekly data time series. Furthermore, we used eXtreme Gradient Boosting (XGBoost) for extracting features from the data. After feature selection we used XGBoost for the purpose of forecasting the electricity load for single time lag. XGBoost perform extremely well for time series prediction with efficient computing time and memmory resources usage. Our proposed scheme outperformed other schemes for mean average percentage error metric.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Raza Abid Abbasi
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Muhammad Nauman Javid Ghuman
    • 2
  • Zahoor Ali Khan
    • 3
  • Shujat Ur Rehman
    • 2
  • Amanullah
    • 2
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.Quaid-i-Azam UniversityIslamabadPakistan
  3. 3.Computer Information ScienceHigher Colleges of TechnologyFujairahUAE

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