Description
Machine learning models are developed and integrated into various applications for process automation and data analysis, enabling systems to learn from data, make predictions, and optimize operations without human intervention. The development of these models involves training algorithms using historical data, followed by continuous improvements as new data is collected, allowing the models to become more accurate and effective over time. In the context of the nZEB Smart Home, machine learning models play a key role in optimizing energy usage, enhancing user comfort, and improving system efficiency. For example, ML algorithms can predict energy consumption patterns, adjust heating and cooling based on user behavior, and manage renewable energy production and storage. As the system learns from real-time data, it can optimize energy flow, reduce waste, and enhance the overall performance of the home. This case study highlights how integrating machine learning into smart home systems not only improves user experience but also contributes to the efficient management of resources, aligning with sustainability goals in nearly Zero Energy Buildings (nZEB). By continuously refining algorithms with incoming data, the system evolves to better meet the needs of its users while supporting energy-efficient living.
