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Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models

Author

Listed:
  • Venkataramana Veeramsetty

    (Center for Artificial Intelligence and Deep Learning, Department of Electrical and Electronics Engineering, S R Engineering College, Warangal 506371, India)

  • Arjun Mohnot

    (Department of Computer Science Engineering, Bennett University, Greater Noida 201310, India)

  • Gaurav Singal

    (Department of Computer Science Engineering, Bennett University, Greater Noida 201310, India)

  • Surender Reddy Salkuti

    (Department of Railroad and Electrical Engineering, Woosong University, Daejeon 34606, Korea)

Abstract

Electric power load forecasting is an essential task in the power system restructured environment for successful trading of power in energy exchange and economic operation. In this paper, various regression models have been used to predict the active power load. Model optimization with dimensionality reduction has been done by observing correlation among original input features. Load data has been collected from a 33/11 kV substation near Kakathiya University in Warangal. The regression models with available load data have been trained and tested using Microsoft Azure services. Based on the results analysis it has been observed that the proposed regression models predict the demand on substation with better accuracy.

Suggested Citation

  • Venkataramana Veeramsetty & Arjun Mohnot & Gaurav Singal & Surender Reddy Salkuti, 2021. "Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models," Energies, MDPI, vol. 14(11), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:2981-:d:559213
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    References listed on IDEAS

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    Cited by:

    1. Hao Yang & Maoyu Ran & Chaoqun Zhuang, 2022. "Prediction of Building Electricity Consumption Based on Joinpoint−Multiple Linear Regression," Energies, MDPI, vol. 15(22), pages 1-19, November.
    2. Venkataramana Veeramsetty & Pravallika Jadhav & Eslavath Ramesh & Srividya Srinivasula, 2024. "Zero crossing point detection in a distorted sinusoidal signal using random forest classifier," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(10), pages 4806-4824, October.
    3. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
    4. Surender Reddy Salkuti, 2022. "Emerging and Advanced Green Energy Technologies for Sustainable and Resilient Future Grid," Energies, MDPI, vol. 15(18), pages 1-7, September.

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