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

B. V. Surya Vardhan, Mohan Khedkar, Ishan Srivastava, Prajwal Thakre and Neeraj Dhanraj Bokde ()
Additional contact information
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

Energies, 2023, vol. 16, issue 3, 1-21

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%.

Keywords: short term load forecasting; machine learning; Bayesian optimization; grid search; random search (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/3/1243/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/3/1243/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:3:p:1243-:d:1045096

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().