Energy-smart buildings

Smart buildings
Buildings, including homes, malls, factories, and hospitals, contribute to 37% of global carbon emissions, with energy consumption expected to rise by 70% by 2030 due to population growth and rising living standards. However, this can be mitigated by using AI-based smart systems connected to sensors in buildings, potentially reducing energy consumption and carbon emissions by up to 30% through optimized lighting, heating, and cooling.
Data challenges
Smart buildings generate vast amounts of data from sensors and connected devices. Conventional techniques that centralize this data for analysis are not applicable in all cases due to various data transfer barriers such as regulations such as regional data transfer legislations, the data might be sensitive from a business perspective and/or owned by someone else, or, the volume of data might simply be so large that data transfer becomes a practical problem.
Federated learning
Federated learning addresses these challenges by enabling local processing of data and model training on edge hardware (e.g. gateway servers in the buildings).

By learning from localized data, federated learning not only upholds user privacy but can also craft baseline models that effectively learn from different buildings or building zones.

These generalizable models can then be further optimized for specific buildings and homes. Furthermore, this machine learning framework enables devices to collaboratively learn from each other, increasing the overall network's capabilities to optimize energy efficiency across multiple buildings.

Last but not least, the concept of federated learning enables better management of combined geographical or property-specific models which can be adapted for new homes and buildings that lack prior training data.