Welcome all who are attending this year's hackathon at the University of California--Davis (for more detail and the event schedule, see https://hackdavis.io). OSIsoft is the data sponsor of this event and this year our focus is data analytics using the facilities information systems at UC Davis. We have consolidated everything you need for a successful project here on PI Developer's Club.
OSIsoft and UCDavis Energy & Data Group Challenge
We have real time data from many meters on campus that measure building electricity, heating (steam/condensate), cooling (chilled water), gas, and domestic water usage. We also have meta data on buildings (square footage, building type, construction date). The dataset is comprise of a large set of real-time time series data. One example of use is here: UC Davis CEED site: http://ceed.ucdavis.edu/
We also have real time data on number of connected users to wireless access points on campus. This data is available through an API the allows queries of real time data and through Google Cloud Platform that allows access to a fixed time range of data.
As referenced above, we have developed visualization of the data, but are seeking deeper analysis or visualizations of the data to get more value and meaning out of the data. Example questions of interest:
- Can predictive models be developed as a function of relevant parameters (ie, air temperatures, building properties, time-based parameters)?
- Can insight be gained on potential energy savings from usage patterns at the buildings as a function of time?
- Are buildings using energy when they should not be?
- How should the buildings be categorized? Are there several unique usage profiles? Are there opportunities for optimization based on real time electricity pricing?
- How can we automate assessment of data quality, detection of outliers, and cleaning of data?
- Are there correlations between wireless occupancy and energy?
- Specific analysis project for wire occupancy data: the access points are labeled with a building ID, but an automated mechanism is needed to accurately map building ID to the correct building location.
We also would like to be able to download the results quickly for larger, more customized queries of the data. These are general guidelines and exemplary ideas. Feel free to think outside the box and come up with innovative ideas that bring more value out of the data.
Prizes will be handed out to 1st, and 2nd place teams for the best use of the OSIsoft PI-WebApi and/or OSIsoft Data Set in GCP. Prize money to be divided up as follows:
- 1st - $500 – Amazon Gift Cards
- 2nd - $250 – Amazon Gift Cards
- Other -$200 – Amazon Gift Cards – Best joint project that joins OSIsoft PI-WebAPI AND Google Cloud Platform
OSIsoft products can be used with other sponsors products. For example machine learning based on building energy consumption, Augmented Reality, Alexa Integration, Esri Integration, etc.
Facilities Data in Google Cloud Storage
OSIsoft has extracted a selection of archival data on several buildings at UCDavis and is available for use on Google Cloud Platform. URLs to the Google Cloud Platform storage buckets are available on the HackDavis 2019 Slack channel.
Access Real Time Facilities Data using OSIsoft PI Web API
In addition to the archival data we have extracted, you also have the ability to directly query UC Davis assets directly in the production system for Water, Electricity and Steam across all the buildings at the university.https://ucd-pi-iis.ou.ad3.ucdavis.edu/piwebapi
Resources to learn PI Web API
If you're not familiar with Web API we suggest you view the following resources:
- Programming in PI Web API
- Introduction to RESTful Services and Web API
- Making HTTP GET Requests to retrieve data
- Introducing Client Library for .NET Framework
- Introducing Client Library for Python
- Introducing Client Library for Angular 4
- Introducing Client Library for Java
- Learn how to use Web API client libraries for Data Science
- To make things easier for larger returns of data in Web API, you should consider a web service testing tool like PostMan
- Client libraries are not required for your project, but if you are more comfortable in a particular programming language (such as Python, etc.) you may find it easier to work within the data structures and programming paradigm of your native programming language rather than hand-coding HTTP datagrams. Further, the client libraries afford you the ability to handle error conditions gracefully.
Also: We're Hiring!
We are presently hiring Product Support Engineers, I/T Support Engineers and Software Engineers. You can apply online to OSIsoft and submit your resume to http://osisoft.com/careers
Campus Energy Data Access
There will be an OSIsoft workshop Saturday at 2:00pm -3:00 pm where OSIsoft and UC Davis Energy & Data Group will outline the API, Dataset and discuss the challenge in detail.