Artificial Intelligence & Machine Learning Application


Project scope
Categories
Data analysis Data modelling Software development Machine learning Artificial intelligenceSkills
machine learning energy management artificial intelligence researchOur company deals with energy management based on space occupancy. The occupancy data is used towards controlling ventilation in any space. We are looking at developing an AI/ML model to predict how a space is expected to be occupied and what would be the needed ventilation requirement for the monitored space.
We would like to collaborate with students to apply the latest artificial intelligence (AI) and machine learning (ML) techniques to our existing dataset. Students will develop an AI / ML model related to any of the aforementioned applications.
This will involve several different steps for the students, including:
- Conducting background research on our existing products and the dataset.
- Analyzing our current dataset.
- Researching the latest AI / ML techniques and how they could be applied to our data.
- Developing an AI / ML model that provides unique outcomes or insights into our data.
- Providing multiple solutions that can be applied to solve the same problem.
The project deliverable should be a clear understanding of the datasets that we are trying to work with to develop a predictive model in terms of occupancy and energy usage.
Providing the most effective versions of models that can be used with our solution
Final deliverables should include
- A final report on the dataset, the problem solved, methodologies and approaches, outcomes and results
- Source materials such as code and workbooks.
We will provide students with access to data sets and also insights on developing the ML product. Students need to have an understanding of databases and programming.
We will provide access to live database.
About the company
Feedback Solutions leverages best-in-class people counting sensors with its patented technology platform to continuously calculate highly accurate real-time occupant counts based on user-defined zones within a building. This data is then communicated via: BACnet/IP, cloud platform or DDC controller – so that ventilation requirement are optimized seamlessly in real-time based on actual occupant demand. These real-time adjustments reduce energy consumption by as much as 40%, reduce GHG emissions and result in less wear and tear on critical HVAC equipment – all while meeting space ventilation requirements as per ASHRAE guidelines.