Introduction
In urban cities such as New York City, buildings are responsible for up to 75% of total greenhouse gas emissions, and over 90% of total benchmarked energy consumption. A significant portion of the energy consumed directly services humans in retail, commercial, and residential buildings. As sustainability increasingly becomes an important factor in modern society, energy consumption in the built environment is one area where reduction can have a major impact.
However, many areas of the built environment do not have the capabilities to measure personal energy consumption, much less notify people of their personal energy responsibility. Most notably, there is a lack of energy and population data with high temporal and spatial granularity. Without sensors to measure this data, or models to estimate this data, energy footprinting is not possible.
CityEnergy is a scalable, real-time system for computing personal energy footprint estimates. As shown in the figure, CityEnergy estimates the energy consumption and population for each building in real-time; these estimates are used to calculate the per capita energy footprint at the building level. CityEnergy is able to provide personal energy footprints to people who provide their location data through a mobile application.
Project Website
The official cityEnergy website can be accessed here.
Publications
The 6th ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys 2019)
Data-Driven Energy and Population Estimation for Real-Time City-Wide Energy Footprinting
Peter Wei, Xiaofan Jiang
Best Paper Runner-Up Award
ACM Combining Physical and Data-Driven Knowledge in Ubiquitous Computing (CPD 2019)
City-Scale Vehicle Tracking and Traffic Flow Estimation using Low Frame-Rate Traffic Cameras
Peter Wei, Haocong Shi, Jiaying Yang, Jingyi Qian, Yinan Ji, Xiaofan Jiang
The 5th ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys 2018)
A data-driven system for city-scale personal energy footprint estimations
Peter Wei, Xiaofan Jiang
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