Smart Decarbonization of the Built Environment in the Nexus of Climate Change, Population Growth and Technology Adoption
The built environment accounts for about 30% of energy consumption and associated greenhouse gas emissions, while also offering a 50-90% mitigation potential. However, creating strategies to unlock this potential is challenging due to the lack of integrated tools. More dramatically, cities need to better integrate urban strategies for mitigation and adaptation to climate change and consider the lock-in risks of their climate responses, all the while taking into account human behavior in terms of technology adoption as well as advances in modern energy management technologies.
To address this, the project develops an integrated computational framework to model (a) the energy demand on a neighborhood scale with building level resolution under projected future climates, (b) potential simultaneous urban transformation pathways, (c) smart, artificial intelligence-based control of the energy systems for efficient operation, and (d) technology diffusion and population behavior due to various policies.
There are a variety of conceivable future pathways, e.g., urban planning scenarios, technology adoption scenarios, climate change, energy efficiency policies, population growth, etc. The integrated simulation framework will be able to answer questions that are exploratory, e.g, What is the range of potential pathways given certain boundary conditions?, or more goal oriented, e.g., What % of the building stock needs to be retrofitted how fast, to what standard and how is that facilitated by policy to achieve a target energy/emission demand?
Integrated energy decision-making: How, when, and why adoption and co-adoption happen, BECC December 2020, Vivek Shastry
MARLISA: Multi-Agent Reinforcement Learning with Iterative Sequential Action Selection for Load Shaping of Grid-Interactive Connected Buildings, ACM BuildSys’2020, November, Jose Vazquez-Canteli