Actionable deep learning under uncertainty for carbon-centric building operations

Operational carbon refers to GHG emissions that come from all energy sources used to keep our buildings warm, cool, ventilated, lighted, and powered. Operational carbon contributes to 28% of global GHG emissions. Reduced global GHG emissions are crucial to combating climate change. For this purpose, the energy assets and appliances should be used considering carbon emissions. The advancement of energy systems and the integration of renewable generations and energy storage have made it possible to operate them when the energy mix has low carbon generations. Given a particular geographic location, the energy should be consumed in its purest form to significantly reduce the operational carbon footprint of the building stock. Some other factors may also be considered such as peak demand and operational cost. This brings out a multi-objective decision-making problem that is set to optimize the asset/appliance operations, thus reshaping the energy consumption profile for a better demand-side response. To address this problem, a smart, proactive solution is needed to account for the future changes of pertinent variables such as carbon intensity and electricity price in producing real-time control decisions.
This project aims to reshape the building consumption profile to maximally track the low-carbon generation at the grid through smart operations of energy assets and devices such as energy storage and flexible demand (e.g., EVs). This will involve the development of a deep learning approach (e.g., deep reinforcement learning) for building energy management factoring in real-time monitoring and prediction of pertinent variables such as carbon intensity. We will incorporate uncertainties and optimize multiple objectives in the space of carbon and energy efficiency when making building control decisions. Further, we will also identify important variables (e.g., time, weather, climate, air quality) that can affect and contribute to the decision-making process. The research is expected to result in a better demand side response for the building stock and a range of benefits including reduced GHG emission, energy cost, and peak demand.