Yu Qian Ang

• Doctor of Philosophy
• Massachusetts Institute of Technology

With buildings accounting for 40% of global carbon emissions, cities striving to meet sustainability targets aligned with the Paris Agreement must retrofit their existing building stock within 30 years. Previous studies have shown that urban building energy models (UBEMs) can help cities identify technology pathways — combinations of energy efficie...

We work with policymakers in eight cities worldwide to identify technology pathways toward their near- and long-term carbon emissions reduction targets for existing buildings. Based on policymakers’ interests, we define city-specific shallow and deep retrofitting packages along with onsite photovoltaic generation potential. Without further grid dec...

Many regions, such as coastal communities, are often located at the end of large transmission lines, leaving them susceptible to power outages and reliant on conventional fuels. We propose a multi-disciplinary, multi-objective optimization method to design a hybrid renewable energy system mix including solar, wind, and marine energy. The method fir...

Cities have been the nexus of economic activity and growth, but they have an insatiable appetite for energy. In response to the challenges and potential impact of climate change, cities and municipalities around the world are developing climate action plans to reduce carbon emissions and enhance resilience of their built environments. However, poli...

This chapter develops a novel approach to predict post-money valuation of startups across various regions and sectors, as well as their probabilities of success. Using startup funding data and descriptions from Crunchbase over a 10-year period, we develop two models linking information such as description, region, and venture capital funding to suc...

Policymakers are struggling to understand what specific mixes of building retrofitting upgrades are necessary to achieve carbon emission targets. Urban building energy modelling (UBEM) is a bottom-up simulation method to develop policy measures for building stocks. However, the use of UBEM tools requires hard-to-find individuals with training in mu...

Urban building energy and daylight modeling are bottom-up, physics-based approaches to simulate the thermal and daylight performance of neighborhoods and cities. The field has flourished in recent years due to a wider accessibility of urban data sets which contain the required information regarding building geometry and program. However, key buildi...

Weather data is a crucial input for myriad applications in the built environment, including building energy modeling and daylight analysis. Building science practitioners and researchers have been able to select from a variety of weather files such as Weather Year for Energy Calculation 2 (WYEC2) and the Typical Meteorological Year (TMY). However,...

Urban building energy modeling (UBEM) is a bottom-up, physics-based approach to simulate the thermal performance of new or existing neighborhoods and cities. The field has flourished in recent years, creating increasingly robust urban data streams that lead from Geographical Information Systems, CityGML, Light Detection and Ranging, and tax assesso...

This chapter develops a novel approach to predict post-money valuation of startups across various regions and sectors, as well as their probabilities of success. Using startup funding data and descriptions from Crunchbase over a ten-year period, we develop two models linking information such as description, region, and venture capital funding to su...