Session: 03-01: Advances in Indoor Environment Technologies and Solutions

Paper Number: 156681

156681 - Urbanflow: Community Spatial Building Energy Data Visualization & Analytics 

Abstract: 

Urban communities face significant challenges in achieving sustainability goals due to the complex interplay of infrastructure, policy, and public engagement. In response, this study introduces the UrbanFlow platform, an interactive, community-oriented web tool designed to support informed decision-making for energy sustainability in urban settings. Developed for Camp Hill Borough, Pennsylvania, this platform leverages the Automatic Building Energy Modeling (AutoBEM) suite and the Model America dataset to simulate and visualize building-specific energy consumption, potential savings, and retrofitting options. This research aims to bridge the gap between technical energy insights and accessible community engagement by offering an intuitive interface that helps local stakeholders, from homeowners to urban planners, make sustainable energy decisions.

Buildings account for approximately 35% of global energy consumption and contribute 38% of total emissions. UrbanFlow harnesses AutoBEM's capabilities to simulate energy consumption for over 3,000 buildings in Camp Hill, integrating the borough’s Climate Action Plan (CAP) objectives. Camp Hill’s CAP targets a 30% greenhouse gas reduction by 2030, with initiatives that include energy audits, renewable energy promotion, and energy provider evaluations. By modeling these goals within the platform, community members can visualize current energy usage, explore potential retrofit measures, and assess the environmental and economic impacts of these changes.

The methodology combines high-resolution spatial data from the Model America database with simulations conducted in AutoBEM, processed via supercomputing resources capable of modeling one million buildings per hour. Through this high processing capacity, UrbanFlow evaluates energy-saving measures, such as heat pump installations, enhanced insulation, solar inverter application, and HVAC electrification. Results show variable impacts, where measures like solar inverters significantly reduce costs, while gas-to-electric HVAC conversion demonstrates increased costs despite reducing natural gas use. These insights are conveyed through an intuitive interface with color-coded energy consumption levels and detailed savings matrices, enabling users to compare retrofit scenarios and select cost-effective, energy-efficient options.

UrbanFlow also incorporates a feedback mechanism that allows community facilitators to gather local input, encouraging stakeholders to participate actively in energy sustainability initiatives. By including socio-spatial data, the platform can support equity-driven analyses, such as energy burden assessments and neighborhood-specific energy strategies, which are critical for ensuring inclusive access to energy sustainability benefits.

In conclusion, the UrbanFlow platform demonstrates the potential of combining building energy modeling with community-focused data visualization. By integrating advanced simulation capabilities with user-friendly design, the platform supports Camp Hill Borough’s CAP goals and offers a replicable model for urban energy sustainability. This approach not only highlights the value of localized energy data but also fosters a collaborative environment where community members and policymakers can work toward shared sustainability objectives. As cities worldwide seek effective solutions to reduce emissions and increase resilience, UrbanFlow presents a promising framework for translating technical insights into community-based action.

Presenting Author: SHOVAN Chowdhury Oak Ridge National Laboratory

Presenting Author Biography: Shovan Chowdhury is a data scientist with expertise in mechanical and energy engineering. He holds a position as a Research Fellow in the Grid-Interactive Controls group at the Building Technologies Research and Integration Center (BTRIC) of Oak Ridge National Laboratory. Prior to this role, he earned a master’s degree in Mechanical Engineering from Idaho State University. At ORNL, Shovan focuses on large-scale building energy modeling and simulation using the AutoBEM software suite. His current projects involve the development of the AutoBEM software suite and the Model America dataset. He specializes in handling big data and implementing machine learning techniques for building energy modeling. Additionally, he has developed future typical meteorological year (fTMY) files for every Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP) scenario for each county in the United States. Shovan contributed to key data releases including data and energy models for 141.5 million U.S. buildings