Open FlippyShivam opened 3 weeks ago
Team onboarding, new people join in, and we all decided what projects we want to join.
List your specific objectives for this week, including learning targets and project milestones.
None yet
Catch up on reading
Catching up
We left on a slight weak note, so we need to push to create a solid program and model
None yet
We split Energy in Buildings into two teams, commercial and residential. I am in Residential.
NA
Meet new team, start onboarding for specific project
This week we all got our assigned teams, and started the onboarding process on what we all did last semester (Spring 24).
List your specific objectives for this week, including learning targets and project milestones.
Held multiple meetings to discuss updates on what research everyone has done. Last team was all CS, and now we have more diversity with architecture students and CS students.
Held meetings, and created respective team group chats for people to get to know each other and communicate goals.
Teach how computational modeling works, and overview on what we looked for.
Most people do not come from a CS background, so it was important to teach them how this modeling project whilst also taking inputs to better understand architectural play as I do not have much experience in that.
Onboarding and playing catch up
Possibly have a workshop to discuss how the Multivariate model was synthesized.
We continued research on modeling and explaining it more in-depth
-Read through some papers, and also held meetings to catch up on what exactly residential may look for over commercial. -Held long meeting to explain further detail how Machine learning works.
Read resources and held meeting
Onboarding, and attempting to still run ASHRAE
ASHRAE is still taking a while, may have to communicate with Dr.Kastner how it may be a lost cause because it takes a long time anyway.
-In-Depth Model Meeting (09/12): reviewed models more in depth, understood that the ASHRAE model is most likely too involved for this project
-Meeting w/ Prof. Kastner (09/13): Start looking into a creating a grasshopper script to create a synthetic dataset to train the model.
Everyone gained a better understanding of project.
-One final push on ASHRAE, if not the focus on models we have now, like Multivariate.
Now build an understanding on how we will tackle residential heating/cooling outputs.
none
Attempt to create synthetic data, with some created via parametric grasshopper script or via https://www.mockaroo.com/
I am confused on why we use synthetic data, when we want to train model with real data.
Built understanding on synthetic data
Collect data then run a base model to see what we can get from Multivariate
Look into ResStock Data, and then understand which variable may be useful. (There are many inputs and outputs).
Ended up running Multivariate Model on 7 variables:
Did analysis on variables that we thought maybe important to the collect total expected energy output.
Fix Multivariate code (some parts had bugs) and continue with better variables.
Understanding which columns in csv to use, and also better understanding why some outputs where not giving results that we would expect.
Ask next week why orientation is not showing expected results, and choose better variables to then build better model.
Get better data, and possibly figure where to input dimensions and why orientation is not shown as significant (it SHOULD!!).
Met team to discuss how to create a better dataset out of ResStock, understand what some variables could mean, and which inputs to run in Multivariate.
Getting further in parametric in grasshopper, so hopefully we can run them and see if we get better results for outputs.
Got a little busy with midterms, hopefully will try to do weekend.
Now we must focus on the math and analysis of the data. ResStock does not give full dimensions, so we must combine our information and see of we can create simulated data and see if it has better correlation. (Big one is orientation).
Weekly Notebook Entry — Week []
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List your specific objectives for this week, including learning targets and project milestones.
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Takeaway, Learning
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