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goruck / nilm

Energy Management Using Real-Time Non-Intrusive Load Monitoring
MIT License
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Help Needed for NILM-based Load Disaggregation of Fans and Mixer Grinders #2

Open YSSAINITISH18052000 opened 2 months ago

YSSAINITISH18052000 commented 2 months ago

Hello everyone,

I am a student working on load disaggregation using a data acquisition device sampling Active Power at 1Hz. I have successfully implemented a few deep learning models for this purpose but none of these seem to give any good results with appliances multiple states especially Fans, Mixer Grinders.

Could anyone provide guidance or point me in the right direction on how to tackle this issue?

Thank you in advance for any help you can provide!

khirds commented 2 months ago

What models have you tested so far ,are you using your own data or available datasets

goruck commented 2 months ago

@YSSAINITISH18052000 I used the REFIT dataset to train the models described in this project but only focused on kettle, microwave, fridge,, dishwasher and washing machine appliance types. However, the REFIT data set does contain limited data for fans and grinders/mixers. You can fine tune one of my appliance models with the REFIT data for fans and mixers/grinders and or fine tune with locally generated data or data from another data set. My models work well for appliances with multiple states so this approach should work. If you want to do this, I can send you the weight of my models.

khirds commented 2 months ago

Hi, I am starting my work in NILM can anyone please guide where to start from Please?

On Sat, Sep 28, 2024 at 6:26 PM Lindo St. Angel @.***> wrote:

@YSSAINITISH18052000 https://github.com/YSSAINITISH18052000 I used the REFIT dataset to train the models described in this project but only focused on kettle, microwave, fridge,, dishwasher and washing machine appliance types. However, the REFIT data set does contain limited data for fans and grinders/mixers. You can fine tune one of my appliance models with the REFIT data for fans and mixers/grinders and or fine tune with locally generated data or data from another data set. My models work well for appliances with multiple states so this approach should work.

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