NILM

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https://bitbucket.org/aicip/load-disaggregation

  1. Non-Intrusive Load Monitoring #

![LD](/LD.png)

Energy disaggregation or Non-Intrusive Load Monitoring (NILM) addresses the issue of extracting device-level energy consumption information by monitoring the aggregated signal at one single measurement point without installing meters on each individual device. Energy disaggregation can be formulated as a source separation problem where the aggregated signal is expressed as linear combination of basis vectors in a matrix factorization framework.

Paper: [Non-Intrusive Energy Disaggregation Using Non-negative Matrix Factorization with Sum-to-k Constraint.�](http://ieeexplore.ieee.org/abstract/document/7835299/)


      1. prerequisite:###

MATLAB R2015a

      1. Datasets###

We design two different experiments for evaluating our proposed algorithm. The first experiment is disaggregation of the whole home energy to the energy consumption of all the appliances at a residential home. For this part we use the [AMPds](http://ampds.org/): A Public Dataset for Load Disaggregation and Eco-Feedback Research for the first experiment. This dataset has most required features for performing an accurate disaggregation task.

The second experiment is designing a hierarchical scheme for disaggregating the whole building energy signal to the HVAC components signals in an industrial building.

![Diag](blockdiag1.PNG)

For this experiment, the data was collected on the Oak Ridge National Laboratory (ORNL) Flexible Research Platform (FRP1). FRP1 was constructed to enable research into building envelope materials, construction methods, building equipment, and building instrumentation, control, and fault detection. Please see the data folder and ([The ORNL dataset details.](/data/ORNL_data_info.zip)) for details of all the collected data in different time spans during the year in FRP1 and FRP2. For getting the whole ORNL data please contact [Alireza Rahimpour](mailto:arahimpo@utk.edu).

      1. Demo###

Run the [`Demo.m`](/Demo.m) to see the result of disaggregation algorithm on the AMPds dataset. Please put the [`AMP_DATA.mat`](/AMP_DATA.mat) in the same folder when running the [`Demo.m`](/Demo.m).


  • If parameter `training=1`, code performs the signal decomposition only without prediction.
  • For prediction: `training=0`.
  • Please see the guide in the beginning of the demo code to see how you can apply different methods such as Non-negative Sparse coding and Elastic Net.


      1. Results###

You should see the following results (and a lot more!) after running the demo.

  • Ground truth and estimated appliances’ signals using the S2K-NMF method for one random testing day (1440 minutes).

![f1](alldev2.png)

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  • The pie plots show that S2K-NMF achieves the best result for estimating the energy usage contribution of each device:

![f2](pie2.png)


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  • Ground truth (top figure) and estimated aggregated signal (middle

figure) and absolute difference between them (bottom figure) for the residential home in one random testing day (1440 minutes) using the S2K-NMF algorithm. ![f3](AGG_2.png)


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      1. Useful links###
      1. Citation:###

IEEE Transactions on Power Systems Arahimpo (talk) 09:50, 28 March 2018 (EDT) @article{rahimpour2017non, title={Non-Intrusive Energy Disaggregation Using Non-negative Matrix Factorization with Sum-to-k Constraint}, author={Rahimpour, Alireza and Qi, Hairong and Fugate, David and Kuruganti, Teja}, journal={IEEE Transactions on Power Systems}, year={2017}, publisher={IEEE} } Arahimpo (talk) 09:50, 28 March 2018 (EDT)

IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015 Arahimpo (talk) 09:50, 28 March 2018 (EDT) @inproceedings{rahimpour2015non, title={Non-intrusive load monitoring of HVAC components using signal unmixing}, author={Rahimpour, Alireza and Qi, Hairong and Fugate, David and Kuruganti, Teja}, booktitle={IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015}, pages={1012--1016}, year={2015}, organization={IEEE} } Arahimpo (talk) 09:50, 28 March 2018 (EDT)

      1. Contact###

Please feel free to contact [Alireza Rahimpour](mailto:arahimpo@utk.edu) for more information about this project.