Live Case – Project

Incorporating Machine Learning into Cloud Based HVAC Monitoring System

A Collaboration Between

Engagement Synopsis

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

CompanyEco-Enterprise
HQNew Jersey
RevenueN/A
Employees1-5
StageSmall Business
Hiring PotentialFollow-on Projects, Formal Internship, Entry Level Full-Time, Upper Level Full-Time
Websitehttp://Eco-Enterprise.com

Company Overview

eco-enterprise helps businesses to become more Efficient, Economic and Environmentally responsible via unique financial solutions and energy-saving technologies.

Course Info & Engagement Details

SchoolA&S Experiential Hiring Programs
Engagement FormatLive 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.
CourseFacilities Management RFP
Level
  • All Undergraduate
Students Enrolled8
Meeting Day & TimeMonday, Wednesday, Friday: 11:00 AM MT - 11:50 AM MT
Student Time Commitment1-3 Hours Per Week
Company Time Commitment1 Hour
Duration51.57 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

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

     

  • 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.