A Modeling-Based Decision Support System for Enabling Mass Net-Zero Energy Retrofit of Building Communities in Developing Countries

Reducing energy demand in buildings is one of the critical elements of the current climate change mitigation strategies because buildings account for 40% of all energy-related CO2 emissions worldwide. In developed nations where the functional and construction components of the stock are uniform and where the digital twin of the stock has already been built in desirable standard formats for energy simulation exchange, selecting retrofits that optimize energy for the urban building stock is becoming increasingly popular. However, it is challenging to create a similar schema to arrive at energy-efficient retrofits for developing countries where the building stock is highly diverse, with varying construction and operational philosophies, and where there are no readily accessible data sets of existing stock. This study addresses this gap by developing a decentralized customizable decision support system for community-wide annual net-zero planning that policymakers can use for deciding the most efficient building retrofits for reaching the annual net-zero energy targets. The decision support system is deployed to determine a case-study building community's potential for annual net-zero energy use in Mumbai, India. The results demonstrated that the building community's electrical energy-use intensity, as determined by the decision support system, was 29.51 kW·h/m2, with a deficient percent error of 3% from the actual readings. An intriguing finding from the results is that a community can achieve net-zero electrical energy annually when its buildings combine to exchange energy among themselves, even when certain buildings cannot independently achieve zero-energy building status. One building community was found to be precisely close to self-sufficiency, and the other three produced 20%-40% more electrical energy than needed for self-sufficiency.