Description
Machine learning utilizes predictive models to analyze trends, forecast future scenarios, and adapt to dynamic environments, making it an essential tool for optimizing various processes, including energy management. By leveraging these models, systems can anticipate changes, optimize operations, and make data-driven decisions in real time. However, the accuracy and performance of these models heavily depend on the quality of the data used, which is why data pre-processing is a critical step. This process involves cleaning, transforming, and normalizing data to eliminate inconsistencies, reduce noise, and ensure that the input data is suitable for machine learning algorithms, ultimately improving model outcomes. In the case of the nZEB Smart Home, this approach can be applied to optimize energy usage, manage renewable energy systems, and enhance the overall efficiency of the home. The home acts as a pilot site, allowing for real-world testing and validation of machine learning models in a smart, energy-efficient environment. During the seminar, the nZEB Smart Home and its associated projects will be presented as examples of how machine learning can be used to improve the sustainability and performance of smart buildings. The seminar will showcase how predictive models, combined with proper data pre-processing, enable the seamless integration of renewable energy sources, real-time energy monitoring, and optimized control systems, paving the way for the future of sustainable living.
