Predictive machine learning for smart grid demand response and efficiency optimization

Abstract

This paper explores the evolution of smart grids (SGs) and how they enable consumers to schedule household appliances based on demand response programs (DRs) provided by distribution system operators (DSOs). This study looks at and compares four distinct regression models: linear regression, random forest regressor, gradient boosting regressor, and support vector regressor. This is being done because more and more people are using machine learning (ML) methods to make this process better. The models are trained and tested using a dataset that includes a variety of parameters, such as humidity, temperature, and the amount of power used by appliances. Mean squared error (MSE) and R-squared values are two important performance measures that are used to judge these models and see how well they can make predictions. These results reveal that the gradient boosting regressor was the most accurate model for figuring out how much energy smart homes use. This algorithm could be a great tool for better managing energy use because it can figure out the complicated connections between the things that are input and the amount of energy that appliances use. This study makes a big difference in the creation of strong regression models by emphasizing how important it is to be accurate when making predictions. This, in turn, helps to enhance energy sustainability and economic stability in smart home environments.