Inspiration
The inspiration behind this project is to revolutionize commercial building management by harnessing AI and machine learning to provide real-time insights, predictive maintenance, and user-friendly interfaces for building managers. We aim to address the challenges of asset management in the commercial buildings industry and create a more efficient, cost-effective, and sustainable approach that benefits all stakeholders.
What it does
Our project is a comprehensive asset management solution designed for commercial buildings. It employs advanced machine learning algorithms to analyze data from various sources, including operational logs, maintenance history, and seasonal parameters. The system provides real-time status monitoring of critical assets such as HVAC systems, elevators, and electrical panels while also predicting maintenance needs and potential failures. Its user-friendly interface facilitates data-driven information insights for building admins, ensuring efficient resource allocation and proactive maintenance. The project aims to enhance operational efficiency and reduce costs while optimizing the management of commercial building assets.
How we built it
We collected data from various sources including the CBRE challenge provided data. We processed these different sets of data and ran some advance ML algorithms to get insights on the data which was further utilized for the anomaly detection and prescriptive analytics. We computed parameters like Avg. Service time, leeway period for different machines on the basis of various factors. At the end it build a platform which consisted of several dashboards to provide a continuous monitoring over the assets that a user is incharge of and also raising alarms and notification via emails when any particular appliance requires attention.
Challenges we ran into
We were lacking on the suitable data to train the models and get insights from them. We had several UI and backend integration issues. We had challenges in selecting the best model for some prediction use cases.
Accomplishments that we're proud of
We had a plan, and I would like to say that were able to finish all the story points by following this plan. We handled the variance in the dimension of the solutions. We have build a working website which is extracting and displaying the real time data.
What we learned
We learned a lot about team work ,project management and technology expansion. Got to learn new techniques to use in the data insights speculations.
What's next for Lens for CBRE
A full fledged one stop solution for all the building administration related work like work order raise, vendor switch, machine health monitoring, service recommendations, etc. For starter it would require a much larger amount of data set to have a better reach and accuracy on the real world scenarios.
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