Gaussian process regression-based load forecasting model

In this paper, Gaussian Process Regression (GPR)-based models which use the Bayesian approach to regression analysis problem such as load forecasting (LF) are proposed. The GPR is a non-parametric kernel-based learning method having the ability to provide correct predictions with uncertainty in measurements. The proposed model provides an hourly and monthly load forecast for an Australian city and four Indian cities in the Maharashtra state. Twelve GPR models are trained with historical datasets including hourly load and environmental data. To evaluate the trained model, the actual and predicted load demand curve is plotted and mean average percentage error (MAPE) is calculated corresponding to different kernel functions of the GPR model. To the best of the author's knowledge, the prediction of load demand using GPR for Indian cities of Maharashtra state has been made for the first time. The calculated MAPE in LF is 0.15% for Australia and 0.002%, 0.209%, 0.077%, and 0.140% for Indian cities viz. Nasik, Bhusawal, Kolhapur, and Aurangabad, respectively. The test results illustrate that minimum MAPE in load prediction is obtained using the proposed model that is GPR with 'Exponential' kernel functions. Furthermore, the comparative analysis with the existing approaches confirms the dominance of the proposed model.

Automated summary

This paper proposes Gaussian Process Regression (GPR)-based models for regression analysis problems like load forecasting. The GPR model is a non-parametric kernel-based learning method that can provide accurate predictions with uncertainty. The proposed model is applied to forecast load demand in an Australian city and four Indian cities in Maharashtra state.