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Abstract 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.

Abstract Load demand forecasting is very important for the management, designing and analysis of an electrical grid system. Load forecasting has progressively become a crucial component of the energy management system with the growth of the smart micro grid. This study presents a new framework to long term load forecasting in the world of electricity power with the help of historical load trends. The main objective of this research work is estimating monthly electricity demand of an Indian state Chhattisgarh, in terms of per day average load demand using a machine learning model—Long Short‐Term Memory (LSTM). This framework considers average of each day load demand for every month of years 2018–2022 and forecasted per day average load demand for each month of the year 2023. Furthermore, the predicting accuracy is evaluated for training and testing phase, in terms of error metrics like Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The MAPE values under the training and testing phase are in the range of 0.010%–0.652% and 0.378%–10.54%, respectively. A comparative study of LSTM model with Artificial Neural Network (ANN) model indicates the proposed LSTM model is more accurate and can be applied for real time load demand forecasting.