Leveraging Machine Learning for Sustainable IoT: Predictive Analytics for Energy Efficiency

The interconnection of the Internet of Things (IoT) and machine learning (ML) algorithms makes it possible to significantly support the sustainability and energy efficiency of IoT. This article targets the use of predictive analysis in IoT devices and networks to drive energy savings and cut operational costs in addition to reducing the environmental burden. The objective is to use ML algorithms to create amenities of the Internet of Things that are quick to recognize and guide accordingly in a real-world scenario, where energy demands are in the continual process of change. The research entailed a thorough examination of current practices, an investigation of the case studies on identifying successful algorithms, and the development of a unique ML framework for energy-efficient IoT operations. The predictive model illustrates the achievement of a Mean Absolute Error (MAE) of about 1.34 and a Root Mean Squared Error (RMSE) of about 1.51, which is a good signal that there are few prediction errors in general and that the tool runs robustly. Nonetheless, the model's R-squared value of 0.0492 points to a modest ability to explain phenomena, accounting for just 4.92% of the variation in heating demand. The results convey that a predictive model will be the key to a green future for IoT, this in turn will lead to sustainable development and environment preservation.