# Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

**Authors:** Jack Kelly, William Knottenbelt  
**arXiv:** [1507.06594](https://arxiv.org/abs/1507.06594)  
**Date:** 2015-07-23  
**Summary page:** https://www.summarizepaper.com/en/paper/1507.06594v1/

## Key points

- The paper explores the use of deep neural networks in energy disaggregation
- Deep neural networks have shown remarkable improvements in classification performance in other fields
- Three different deep neural network architectures are adapted for energy disaggregation: LSTM, denoising autoencoders, and a regression network
- Real aggregate power data from five appliances are used to evaluate the performance of these algorithms
- All three neural nets achieve better F1 scores compared to combinatorial optimization or factorial hidden Markov models
- Deep neural networks can accurately estimate individual appliance electricity consumption using only a single meter reading

## Summary

, , , , 

The paper titled "Neural NILM: Deep Neural Networks Applied to Energy Disaggregation" by Jack Kelly and William Knottenbelt explores the use of deep neural networks in energy disaggregation. This process involves estimating appliance-by-appliance electricity consumption from a single meter that measures the overall electricity demand of a home. The authors highlight the remarkable improvements in classification performance achieved by deep neural networks in fields such as image classification and automatic speech recognition. In this study, three different deep neural network architectures are adapted for energy disaggregation: a recurrent neural network called "long short-term memory" (LSTM), denoising autoencoders, and a network that regresses the start time, end time, and average power demand of each appliance activation. Real aggregate power data from five appliances are used to evaluate the performance of these algorithms against seven metrics on both seen and unseen houses during training. The results show that all three neural nets achieve better F1 scores when averaged over all five appliances compared to combinatorial optimization or factorial hidden Markov models. This paper demonstrates how deep neural networks can be effectively applied to energy disaggregation tasks, leveraging advanced machine learning techniques to accurately estimate individual appliance electricity consumption using only a single meter reading for an entire home.