General updates

• Lucas brought planning stuff to a close over the last few weeks
• Looked into Auto-ML stuff
• Singapore officers are still pretty closed, not fertile grounds for a good game until at least July, or midway through July
  • Singapore has moved into initial phase of re-opening stuff
• For the next month or so, tie up work on planning, and then bring together the RL and planning work together

Akaash RL work

• Submitted clustering paper to ACEEE conference
• Came back with thorough list of revisions First point:

1. Paper is pretty technical for the conference, building and energy saving programs
   • Could target for a less technical audience
   • Make the flow of the paper more clear
2. To clear up the flow of the paper, wanted to create a box and arrow diagram of the methods
   • Both Lucas and Akaash created a draft, and see if they can edit it and combine different aspects together

Akaash’s flowchart of the model

1. Choose a supervised learning model, that we can use once we have predicted clusters. The dependent variable of the supervised learning problem is the energy change after the game. The clusters that have the greatest strength in predicting which groups would have a change in energy after the game is the best one
2. Choose a clustering approach - k means
3. Apply the regression on all data, and then on just the clusters, and evaluate the output
   • Look at p-values, and see if they are statistically significant

Lucas’s flowchart

Given energy data, you can either

1. Segment data - energy data before experiment
   • Different types of clusters, kmeans, gmm, etc
   • Evaluate performance predicting energy after the experiment
   • “Winning clustering regime”
   • Persistence on most responsive cluster
2. Regression on the whole dataset
   • General persistence of the energy saving behavior

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uid: 202006031828 tags: #raise #meetings

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