Nānā: To see, observe, or inspect Ikehu: Power, intensity, or energy
What it does
This app visualizes energy usage throughout the University of Hawaii campus through the use of graphs and maps. Users are able to see the amount of energy used for each building by either clicking a building on the campus map, or by selecting a building through the drop down menu.
Raw database files from Aurora BPA MS-SQL can be exported to CSV for import into this application. Three files must be placed in
app/private/files before deploying.
export.csv - Raw data
SampleTsUtc is a timestamp, we do our best to parse any given format
TagLogId matches a meter tag
Mean is the main measurement used for display
Min is used to calculate least demand
Max is used to calculate peak demand
This file can be automatically generated using Aurora BPA MS-SQL with this sample query:
JOIN LogHourExtension on LogHour.TagLogId = LogHourExtension.TagLogId AND
LogHour.DayId = LogHourExtension.DayId AND
LogHour.TimeId = LogHourExtension.TimeId
JOIN tags.dbo.[TagIds] on LogHour.TagLogId = [TagIds].TagLogId
where (TagName = 'kw') and Quality = 1;
This query will select needed header fields, reconcile DayId and TimeId values from the Extension table, then filter out high quality power data
BuildingList.csv - Building configuration
Column 1 supplies a unique tag or identifier for a building
Column 2 is a friendly name for a building
Columns 3,4,5 supply gross square footage, floor count, and room count for the building
TagIds.csv - Meter configuration
BuildingName is a column that matches a building Name
EntityName is a column that supplies a name for a meter
TagLogId is a column that matches the a meter's tag in the raw data
TagName contains that meter's unit of measurement
If you are not familiar with Meteor it is recommended that you Galaxy or mup to deploy this application.
How we built it
The technologies used include:
- Victory (Graphs)
Challenges we ran into
During the HACC, we ran into many challenges. It was difficult finding a way to handle such a large amount of data for our app without slowing it down. We also ran into problems with scheduling and role assignments. Due to time constraints and our members busy schedules, we were not able to work on our app as much as we wanted to.
What we learned
This Hackathon demanded of us (in a good way) to build a project portfolio and most importantly, it gives an opportunity for us to learn new experiences with our team. We've also learned how to manage our time since the duration of the hackathon is in a very short term, this way actually encouraging us to work with the most productive and efficient method because of limited time.
Accomplishments that we're proud of and todo
As a team, we are proud of the fact that our application works the way we want to. We see value in providing tighter integration with other data sources and plan to add REST APIs. These APIs will allow other teams to get data from our application transparently as well as allow users to import data in real time using HTTP. We also would like to develop more historical tracking and metrics to further insights.
Special thanks to:
Miles Topping, Director of Energy Management at University of Hawaii for providing us gigabytes of data and resources for this challenge
Dr.-Ing. Darren Carlson, Professor of Computer Engineering for providing beefy computer and space resources
UH Manoa ICS Department for supplying the inital template for Meteor and React
Formidable Labs for supplying the Victory graphing engine
Papa Parse for the powerful, in-browser CSV parser for big data