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Data Analytics for Electricity Load and Price Forecasting in the Smart Grid

  • Syeda Aimal
  • Nadeem JavaidEmail author
  • Amjad Rehman
  • Nasir Ayub
  • Tanzeela Sultana
  • Aroosa Tahir
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)

Abstract

The present strategies for the prediction of price and load may be difficult to deal with huge amount of load and price data. To resolve the problem, three modules are incorporated within the model. Firstly, the fusion of Decision Tree (DT) and Random Forest (RF) are used for feature selection and to remove the redundancy among feature. Secondly, Recursive Feature Elimination (RFE) is taken for feature extraction purpose that extracts the principle components and also used for dimensionality reduction. Finally, to forecast load and price, Support Vector Machine (SVM) and Logistic Regression (LR) as a classifiers are used through which we achieve good accuracy results in load and price prediction.

Notes

Acknowledgments

This research is supported by Al Yamamah university Riyadh Saudi Arabia.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Syeda Aimal
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Amjad Rehman
    • 2
  • Nasir Ayub
    • 1
  • Tanzeela Sultana
    • 1
  • Aroosa Tahir
    • 3
  1. 1.COMSATS UniversityIslamabadPakistan
  2. 2.Al Yamamah UniversityRiyadhSaudi Arabia
  3. 3.Sardar Bahadur Khan Women UniversityQuettaPakistan

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