Year: 2025
Type: conference-paper
Abstract: Buildings account to nearly one-third of global energy consumption and carbon emissions, making their optimization essential in combating climate change.Cyber-Physical Buildings, enabled by the integration of Internet-of-Things (IoT) devices and advanced data analytics tools like AI, offer a smart and effective approach to energy management.A key challenge, however, lies in automating the semantic labeling of IoT devices to ensure machine-interpretable data.The "Brick-by-Brick: Cyber-Physical Building Data Classification Challenge" aims to tackle this challenge by classifying time-series data from IoT devices within buildings.Participants will engage with a dataset consisting of over 10,000 time-series streams collected over three years across three buildings, representing 91 unique semantic classes.Both the dataset and baselines are established in a published paper.With a total prize pool of 20,000 AUD, the competition is ready to launch in December 2024 and run through February 2025, hosted by AIcrowd.This challenge invites researchers, practitioners, and technologists to drive AIenabled solutions for advancing the next generation of environmentally sustainable cyber-physical buildings.Additional details on the dataset, benchmark, and code can be found in the official repository (https://github.com/cruiseresearchgroup/DIEF_BTS).The challenge was published on AIcrowd www.aicrowd.com/challenges/brick-by- brick-2024.
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410637825