A Deep Learning Technique using Low Sampling rate for residential Non Intrusive Load Monitoring

TL;DR

This paper introduces a deep learning approach combining CNN and LSTM to perform non-intrusive load monitoring using low sampling rate data, enabling detailed appliance energy disaggregation in residential settings.

Contribution

The paper presents a novel CNN-LSTM neural network architecture optimized for low-frequency data, improving load disaggregation accuracy over existing methods.

Findings

Outperforms state-of-the-art on REDD dataset

Effective on low sampling rate data

Suitable for offline low-power devices

Abstract

Individual device loads and energy consumption feedback is one of the important approaches for pursuing users to save energy in residences. This can help in identifying faulty devices and wasted energy by devices when left On unused. The main challenge is to identity and estimate the energy consumption of individual devices without intrusive sensors on each device. Non-intrusive load monitoring (NILM) or energy disaggregation, is a blind source separation problem which requires a system to estimate the electricity usage of individual appliances from the aggregated household energy consumption. In this paper, we propose a novel deep neural network-based approach for performing load disaggregation on low frequency power data obtained from residential households. We combine a series of one-dimensional Convolutional Neural Networks and Long Short Term Memory (1D CNN-LSTM) to extract features that can identify active appliances and retrieve their power consumption given the aggregated household power value. We used CNNs to extract features from main readings in a given time frame and then used those features to classify if a given appliance is active at that time period or not. Following that, the extracted features are used to model a generation problem using LSTM. We train the LSTM to generate the disaggregated energy consumption of a particular appliance. Our neural network is capable of generating detailed feedback of demand-side, providing vital insights to the end-user about their electricity consumption. The algorithm was designed for low power offline devices such as ESP32. Empirical calculations show that our model outperforms the state-of-the-art on the Reference Energy Disaggregation Dataset (REDD).