Skip to content

CIKM19' Active Collaborative Sensing for Energy Breakdown

Notifications You must be signed in to change notification settings

EasecureLab/ActSense

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

12 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

ActSense

This is an implementation for the paper titled "Active Collaborative Sensing for Energy Breakdown" which is published at CIKM 2019. We public our source code in this repository.

Algorithm

ActSense model amis at minimizing the deployment cost by selectively deploying sensing hardware to a subset of homes and appliances while maximizing the reconstruction accuracy of sub-metered readings in non-instrumented homes.

We perform this active sensor deployment via active tensor completion with streaming data. Specifically, at the end of each month, we query the home and appliance pairs that have the highest uncertainty in the current tensor reconstruction, which we prove to reduce reconstruction uncertainty mostly rapidly. And to project a model's prediction uncertainty of future readings in a longer term, we incorporate external seasonal information into model estimationm, which helps the model react to future season changes earlier. The detailed algorithm can be found in the paper.

Usage

To run the code to generate experimental results like those found in our papers, you will need to run a command in the following format, using Python 3:

For our proposed method ActSense

$ cd code
$ python active_sensing.py [--year] [--dataset] [--method] [--init] 
                           [--uncertainty] [--alpha1] [--alpha2] [--alpha3]
                           [--k] [--latent_dimension] [--season_type] 
                           [--regularization] [--gamma1] [--gamma2]
                           [--lambda1] [--lambda2] [--lambda3]
                           [--kernel] [--sigma]

For baseline: random selection

$ cd code
$ python random_selection.py [--year] [--dataset] [--init] 
                             [--k] [--latent_dimension] [--season_type] 
                             [--regularization]
                             [--lambda1] [--lambda2] [--lambda3]

For baseline: query by committee

$ cd code
$ python query_by_committee.py [--year] [--dataset] [--init] 
                               [--k] [--latent_dimension] [--season_type] 
                               [--regularization]
                               [--lambda1] [--lambda2] [--lambda3]

The results will be stored in ../data/result/

For baseline: VBV

We use this implementation.

Dataset

We use the Dataport dataset for evaluation purpose. It is thelargest public residential home energy dataset, which containsthe appliance-level and household aggregate energy consumptionsampled every minute from 2012 to 2018.

We filter out the appliances with poor data quality (large proportion of missing values) to select a subset of them. We get 4 different datasets from year 2014 to 2017 containing 53, 93, 73, and 44 homes respectively and six appliances: air-conditioning (HVAC),fridge, washing machine, furnace, microwave and dishwasher. Onthis selected data set, we reconstruct the aggregate reading by thesum of the selected appliances

Citation

If you use this code to produce results for your scientific publication, please refer to our CIKM 2019 paper:

About

CIKM19' Active Collaborative Sensing for Energy Breakdown

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 65.4%
  • Python 34.6%