Very Short-Term Load Forecasting Using Gaussian Process Regression

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.