This paper presents a comprehensive study on the implementation of a day-ahead machine learning algorithm in residential settings for energy saving purposes. The core of our methodology involves the utilization of the K-Nearest Neighbors (KNN) algorithm for temperature predictions, contributing to the realization of an adaptive and efficient energy management framework. The system collects temperature data at 5-min intervals through temperature sensors, processes it through EMHASS, and trains the KNN model. To address weather-induced variations in thermal energy systems, an automation script checks for foggy conditions, triggering the ‘tune’ endpoint to adapt the KNN model dynamically. Over the month of November, the model has been evaluated on two renewable energy systems: a hot water system and a space heating system. The findings highlight the effectiveness of our approach in predicting thermal energy system’s temperature with a coefficient of determination R2 evaluated at 91% for water heating and 68% for space heating. Marking a significant stride in the domain of smart home energy management.