Live Case – Project
Incorporating Machine Learning into Cloud Based HVAC Monitoring System
A Collaboration Between
Interns will have an opportunity to work on adding ML capabilities to our Industrial Internet of Things (IIOT) platform, and related toolsets and technology. Our IIOT solution is a big data cloud application that captures 31,536,000 rows of data containing over 1.5B data points every year for each device deployed.
Company Information
Company | Eco-Enterprise |
HQ | New Jersey |
Revenue | N/A |
Employees | 1-5 |
Stage | Small Business |
Hiring Potential | Follow-on Projects, Formal Internship, Entry Level Full-Time, Upper Level Full-Time |
Website | http://Eco-Enterprise.com |
Company Overview
Course Info & Engagement Details
School | A&S Experiential Hiring Programs |
Engagement Format | Live Case - Class Collaboration or Case Competition - This learning format allows educators to deliver experiential learning to students at scale. Students are often split into groups to work on a live case (or a series of cases) from a real host company that directly relates to key learning objectives. |
Course | Facilities Management RFP |
Level |
|
Students Enrolled | 8 |
Meeting Day & Time | Monday, Wednesday, Friday: 11:00 AM MT - 11:50 AM MT |
Student Time Commitment | 1-3 Hours Per Week |
Company Time Commitment | 1 Hour |
Duration | 51.57 Weeks |
Project Topics

Architecture, Engineering, Construction (AEC)

Corporate Social Responsibility

Environmental Sustainability & Conservation

Facility Management

Legal, Regulatory, Compliance

Location Analytics

Operations
Students
There are currently no students assigned.
Collaboration Timeline
Touchpoints & Assignments | Due Date | Type | |
---|---|---|---|
Deadline for Students to Register
Deadline for Students to Register
Students register to the course by this date.
|
January 13th, 2021 | Event na | |
Students Review Onboarding Materials
Students Review Onboarding Materials
Students familiarize themselves with onboarding materials and project scope. https://www.microprediction.com/knowledge-center
https://asviablesolutions.com/projects/help-build-the-open-source-prediction-network/
|
January 22nd, 2021 | Event na | |
Official Project Launch
Official Project Launch
11:00 AM MT: We’ll web conference via Zoom you into our class to kickoff the project.
https://umontana.zoom.us/j/95405051063?pwd=Ulo1ek1UaWgrRjAweks1aWZEaWUzZz09
|
January 25th, 2021 | Event na | |
Kickoff Evaluation Due
Kickoff Evaluation Due |
February 5th, 2021 | Event na | |
Milestone Deliverable #1 Due
Milestone Deliverable #1 Due
Please upload your deliverable for milestone #1
|
February 27th, 2021 | Submission Required submission-required | |
Temperature Check Survey Due
Temperature Check Survey Due |
March 5th, 2021 | Event na | |
FINAL PRESENTATIONS
FINAL PRESENTATIONS
We’ll find a time on this day to web conference via Zoom to close the project.
https://umontana.zoom.us/j/95405051063?pwd=Ulo1ek1UaWgrRjAweks1aWZEaWUzZz09
|
April 23rd, 2021 | Event na | |
Milestone Deliverable #2 Due
Milestone Deliverable #2 Due
Please upload your final presentation for Milestone #2
|
April 23rd, 2021 | Submission Required submission-required | |
End of Term SELF Evaluation
End of Term SELF Evaluation |
April 30th, 2021 | Event na | |
End of Term PEER Evaluation
End of Term PEER Evaluation |
April 30th, 2021 | Event na |
Key Milestones & Project Process
-
February 15, 2022 - Deep Dive into Eco-Enterprise and their services/model
- What is Eco-Enterprise’s mission?
- What services are offered?
- What’s the importance of sustainability and efficiency?
- What impact do these initiatives have on economics, financials, and business strategy?
- Who are their competitors?
- In general, what does eco-enterprise and eco-system look to accomplish?
- What are the key data points within HVAC?
- How is this data currently collected?
- What are the key metrics and terms used in HVAC?
Suggested Deliverable:
1-2 page overview on Eco-Enterprise to demonstrate understanding
-
February 22, 2022 - Deep Dive into HVAC system reports and data
- What are best practices when working with HVAC data?
- What trends do you see in these reports/data?
- What kind of anomalies occur within HVAC data?
- What tools are available to process HVAC data reports?
Suggested Deliverable:
Begin qualitative analysis on data provided by eco-enterprise. Take note of any anomalies and trends and put together a data analysis report.
-
March 8, 2022 - Data Analysis Report + Presentation
Using your data analysis tools and knowledge from the previous milestone, present your findings
- Is there a way to detect anomalies through data analysis tools? yes, ML programs such as Random Forests
- Is there a way to improve data collection? Measures of pressures could be added
- Can we detect bad sensors? Yes, this part is easy
- Can we visualize the data to detect anomalies and unexpected events? yes, using Archetypal Analysis
- What are the best tools in predictive analytics to use in HVAC data processing? Random Forests, Hierarchical Clustering, Archetypal Analysis are all useful.
- What’s the best way to implement? Ideally the data is collected and analyzed on the fly. Clusters are developed based on historical data, incoming data is first processed to detect gross errors (compressors off, power down, etc), then compared with historical data clusters. Outliers are potential faults, and are sent for further analysis.
- What’s the strategy to move forward? Develop library of data clusters seen in usual operation for all months of Macy’s data.
Suggested Deliverable:
Data Analysis Report + Presentation
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March 22, 2022 - Final Presentation of ML results on PowerTron HVAC data
With the information we have about variables in the data set, we developed summary measures for each day of each month, that can be used in by ML algorithm to detect variations in day-to-day and month-to-month performance of HVAC machines. Examples of results using this data from standard ML algorithms, Random Forest and Hierarchical Clustering are compared with those from Archetypal Analysis, a more flexible clustering algorithm. Pitfalls and drawbacks are discussed, as well as potential steps forward.
Suggested Deliverable:
Final Presentation and Report
Project Resources
There are no resources currently available
Company Supervising Team
There are currently no supervisors assigned.
School Supervisors
There are currently no supervisors assigned.