Powering the future of electrical load forecasting using a regression learner in machine learning

The primary intent of the present research was to design and execute an electrical load forecasting system using machine learning (ML) techniques. The implementation of an advanced predictive method, specifically an ML algorithm, helped in accurate load forecasting, which is crucial for efficient power grid management, and optimizing resource allocation. Electricity load fluctuates due to various complex factors, making traditional forecasting methods struggle. This is where ML shines. ML algorithms can learn from historical data, identifying intricate patterns and relationships that influence electricity demand. This allows them to make more accurate predictions than static models. In this work, regression learning models in ML are used with the MATLAB platform. Three years of real-time data from the Wavi substation in India are used. Considering day, date, hour of day, max and min temperature of the day, and voltage and current are taken as input parameters to test fourteen different models of assorted regression algorithms. The performance of these models is evaluated using commonly used metrics, root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE), along with a few other parameters. The optimized trained model is then tested with real data to obtain the forecasted load. The correlation between the Actual load and forecasted load is found to be 0.999962.