Deep Neural Networks applied to Energy Disaggregation

- Bhushan Sonawane

Energy Disaggregation (NILM)?

Estimating power demand of individual appliance from a single meter.

Motivation

Motivation

• Itemized electricity bills from a single meter
• Use information at User level or at Grid management
• Identify Faulty devices
• Survey appliance usage behavior

Demo

Let's talk about paper

Neural NILM: Deep Neural Networks applied to Energy Disaggregation

• Author (Imperial College London)
  • Jack Kelly
  • William Knottenbelt
• Link: 
  • https://arxiv.org/abs/1507.06594

Goal

1. Adapt three DNN architecture

2. Comparing Benchmark

• Combinatorial algorithm
• Factorial Hidden Markov Models

3. How well generalizes to unseen house?

Training Data

UK-DALE dataset

• Aggregate main power sampled every 6 second
• Trained one network per target appliance

Choice of appliances

• Appliances chosen-
  • Fridge, Washing Machine, Dish Washer, Kettle and Microwave
• Small appliances cannot be used .

Houses used for training and testing

Let's move on to Networks

1. Recurrent Neural Netowork

Read more

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

INPUT: Aggregate power data

OUTPUT: Sample of power data for the target appliance

2. Denoising Autoencoders

Tries to reconstruct input

Given a input(noisy) aggregate power, 

reconstruct power demand of target appliance

3. Regress Start, End Time & Power

Draw rectangle around each of the appliance activation in aggregate data

Results

Summary

• Author used NILMTK's benchmark implementation of CO and FHMM
• dAE and Regressing outperforms CO and FHMM
• LSTM outperforms CO and FHMM on two-state appliances but falls behind on multi-state appliances

We just learnt about Precision and Recall

Questions?