# 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

# What does the meter sees?

# Demo

# Let's talk about paper

## Neural NILM: Deep Neural Networks applied to Energy Disaggregation

- Author (Imperial College London)
- Jack Kelly
- William Knottenbelt

- Link:

# Goal

## 1. Adapt three DNN architecture

- Long Short-Term Memory (LSTM)
- Denoising Autoencoders
- Regressing network

## 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

### 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?

#### Deep Neural Networks applied to Energy Disagregation

By Bhushan Sonawane

# Deep Neural Networks applied to Energy Disagregation

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