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A Comparative Analysis of Hyperparameter Tuned Stochastic Short Term Load Forecasting for Power System Operator

Author

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  • B. V. Surya Vardhan

    (Department of Electrical Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India)

  • Mohan Khedkar

    (Department of Electrical Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India)

  • Ishan Srivastava

    (Department of Electrical Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India)

  • Prajwal Thakre

    (Department of Electrical Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India)

  • Neeraj Dhanraj Bokde

    (Center for Quantitative Genetics and Genomics, Aarhus University, 8000 Aarhus, Denmark
    iCLIMATE Aarhus University Interdisciplinary Centre for Climate Change, Foulum, 8830 Tjele, Denmark)

Abstract

Intermittency in the grid creates operational issues for power system operators (PSO). One such intermittent parameter is load. Accurate prediction of the load is the key to proper planning of the power system. This paper uses regression analyses for short-term load forecasting (STLF). Assumed load data are first analyzed and outliers are identified and treated. The cleaned data are fed to regression methods involving Linear Regression, Decision Trees (DT), Support Vector Machine (SVM), Ensemble, Gaussian Process Regression (GPR), and Neural Networks. The best method is identified based on statistical analyses using parameters such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), R 2 , and Prediction Speed. The best method is further optimized with the objective of reducing MSE by tuning hyperparameters using Bayesian Optimization, Grid Search, and Random Search. The algorithms are implemented in Python and Matlab Platforms. It is observed that the best methods obtained for regression analysis and hyperparameter tuning for an assumed data set are Decision Trees and Grid Search, respectively. It is also observed that, due to hyperparameter tuning, the MSE is reduced by 12.98%.

Suggested Citation

  • B. V. Surya Vardhan & Mohan Khedkar & Ishan Srivastava & Prajwal Thakre & Neeraj Dhanraj Bokde, 2023. "A Comparative Analysis of Hyperparameter Tuned Stochastic Short Term Load Forecasting for Power System Operator," Energies, MDPI, vol. 16(3), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1243-:d:1045096
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    References listed on IDEAS

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    1. Ishan Srivastava & Sunil Bhat & B. V. Surya Vardhan & Neeraj Dhanraj Bokde, 2022. "Fault Detection, Isolation and Service Restoration in Modern Power Distribution Systems: A Review," Energies, MDPI, vol. 15(19), pages 1-26, October.
    2. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2020. "An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem," Energies, MDPI, vol. 13(16), pages 1-31, August.
    3. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
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