Very Short-Term Load Forecasting Using Gaussian Process Regression

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 16 | 01 | Page :
    By

    Karan Sati,

  • Dr. Abhishek Yadav,

  1. Ph.D. Scholar, Electrical Engineering Department, College of Technology, Govind Ballabh Pant University of Agriculture & Technology, Pantnagar, Uttarakhand, India
  2. Professor, Electrical Engineering Department, College of Technology, Govind Ballabh Pant University of Agriculture & Technology, Pantnagar, Uttarakhand, India

Abstract

Very Short-Term Load Forecasting (VSTLF) is critical for real-time grid stability, frequency control, and economic dispatch. This study proposes a Gaussian Process Regression (GPR)-based framework for one-hour-ahead load forecasting using hourly data from January 2020 to April 2024 for Delhi, India. The model incorporates meteorological data such as temperature, humidity, and dew point with lagged load values. The research takes into account time-related dependencies and seasonal changes in order to boost the predictive power of the suggested model. Unlike deterministic neural models, GPR provides probabilistic predictions along with uncertainty quantification. Multiple kernel configurations were evaluated across datasets of increasing size (6,000–30,000 samples). The best- performing configuration (Exponential kernel, 25,000 samples) achieved a Testing RMSE of 111.43 MW, MAPE of 2.5349%, MAE of 77.99 MW, and R² of 0.9841. The evaluation highlights the model’s strength when faced with different data sizes and its capacity to deliver stable performance with little overfitting. Results demonstrate that GPR provides stable, accurate, and interpretable forecasting suitable for operational power system applications. The proposed framework presents substantial benefits regarding reliability, scalability, and adaptability for real-time implementation in contemporary smart grid settings, facilitating effective decision-making and enhanced energy management strategies. Adding uncertainty bounds to the mix bolsters operator confidence by facilitating planning that takes risk into account and management of the grid that anticipates problems.

Keywords: Load Forecasting, Very Short-Term Load Forecasting (VSTLF), Gaussian Process Regression, Meteorological Variables, Power System Operation, Forecasting Accuracy

How to cite this article:
Karan Sati, Dr. Abhishek Yadav. Very Short-Term Load Forecasting Using Gaussian Process Regression. Trends in Electrical Engineering. 2026; 16(01):-.
How to cite this URL:
Karan Sati, Dr. Abhishek Yadav. Very Short-Term Load Forecasting Using Gaussian Process Regression. Trends in Electrical Engineering. 2026; 16(01):-. Available from: https://journals.stmjournals.com/tee/article=2026/view=240343


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Ahead of Print Subscription Review Article
Volume 16
01
Received 01/04/2026
Accepted 10/04/2026
Published 20/04/2026
Publication Time 19 Days
1

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