Poster Abstract: Bits and Watts: Improving energy disaggregation performance using power line communication modems

Non-intrusive load monitoring (NILM) or energy disaggregation, aims to disaggregate a household's electricity consumption into constituent appliances. More than three decades of work in NILM has resulted in the development of several novel algorithmic approaches. However, despite these advancements, two core challenges still exist: i) disaggregating low power consumption appliances and ii) distinguishing between multiple instances of similar appliances. These challenges are becoming increasingly important due to an increasing number of appliances and increased usage of electronics in homes. Previous approaches have attempted to solve these problems using expensive hardware involving high sampling rates better suited to laboratory settings, or using additional number of sensors, limiting the ease of deployment. In this work, we explore using commercial-off-the-shelf (COTS) power line communication (PLC) modems as an inexpensive and easy to deploy alternative solution to these problems. We use the reduction in bandwidth between two PLC modems, caused due to the change in PLC modulation scheme when different appliances are operated as a signature for an appliance. Since the noise generated in the powerline is dependent both on type and location of an appliance, we believe that our technique based on PLC modems can be a promising addition for solving NILM

NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring

Non-intrusive load monitoring, or energy disaggregation, aims to separate household energy consumption data collected from a single point of measurement into appliance-level consumption data. In recent years, the field has rapidly expanded due to increased interest as national deployments of smart meters have begun in many countries. However, empirically comparing disaggregation algorithms is currently virtually impossible. This is due to the different data sets used, the lack of reference implementations of these algorithms and the variety of accuracy metrics employed. To address this challenge, we present the Non-intrusive Load Monitoring Toolkit (NILMTK); an open source toolkit designed specifically to enable the comparison of energy disaggregation algorithms in a reproducible manner. This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets. Our toolkit includes parsers for a range of existing data sets, a collection of preprocessing algorithms, a set of statistics for describing data sets, two reference benchmark disaggregation algorithms and a suite of accuracy metrics. We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.Comment: To appear in the fifth International Conference on Future Energy Systems (ACM e-Energy), Cambridge, UK. 201

Demo Abstract: NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets

In this demonstration, we present an open source toolkit for evaluating non-intrusive load monitoring research; a field which aims to disaggregate a household's total electricity consumption into individual appliances. The toolkit contains: a number of importers for existing public data sets, a set of preprocessing and statistics functions, a benchmark disaggregation algorithm and a set of metrics to evaluate the performance of such algorithms. Specifically, this release of the toolkit has been designed to enable the use of large data sets by only loading individual chunks of the whole data set into memory at once for processing, before combining the results of each chunk.Comment: 1st ACM International Conference on Embedded Systems For Energy-Efficient Buildings, 201

PlantDoc: A Dataset for Visual Plant Disease Detection

India loses 35% of the annual crop yield due to plant diseases. Early detection of plant diseases remains difficult due to the lack of lab infrastructure and expertise. In this paper, we explore the possibility of computer vision approaches for scalable and early plant disease detection. The lack of availability of sufficiently large-scale non-lab data set remains a major challenge for enabling vision based plant disease detection. Against this background, we present PlantDoc: a dataset for visual plant disease detection. Our dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images. To show the efficacy of our dataset, we learn 3 models for the task of plant disease classification. Our results show that modelling using our dataset can increase the classification accuracy by up to 31%. We believe that our dataset can help reduce the entry barrier of computer vision techniques in plant disease detection.Comment: 5 Pages, 6 figures, 3 table

Evaluation of low-complexity supervised and unsupervised NILM methods and pre-processing for detection of multistate white goods

According to recent studies by the BBC and the Scottish Fire and Rescue Service, malfunctioning appliances, especially white goods, were responsible for almost 12,000 fires in Great Britain in just over 3 years, and almost everyday in 2019. The top three “offenders” are washing machines, tumble dryers and dishwashers, hence we will focus on these, generally challenging to disaggregate, appliances in this paper. The first step towards remotely assessing safety in the house, e.g., due to appliances not being switched off or appliance malfunction, is by detecting appliance state and consumption from the NILM result generated from smart meter data. While supervised NILM methods are expected to perform best on the house they were trained on, this is not necessarily the case with transfer learning on unseen houses; unsupervised NILM may be a better option. However, unsupervised methods in general tend to be affected by the noise in the form of unknown appliances, varying power levels and signatures. We evaluate the robustness of three well-performing (based on prior studies) low-complexity NILM algorithms in order to determine appliance state and consumption: Decision Tree and KNN (supervised) and DBSCAN (unsupervised), as well as different algorithms for preprocessing to mitigate the effect of noisy data. These are tested on two datasets with different levels of noise, namely REFIT and REDD datasets, resampled to 1 min resolution

SpiroMask: Measuring Lung Function Using Consumer-Grade Masks

According to the World Health Organisation (WHO), 235 million people suffer from respiratory illnesses and four million people die annually due to air pollution. Regular lung health monitoring can lead to prognoses about deteriorating lung health conditions. This paper presents our system SpiroMask that retrofits a microphone in consumer-grade masks (N95 and cloth masks) for continuous lung health monitoring. We evaluate our approach on 48 participants (including 14 with lung health issues) and find that we can estimate parameters such as lung volume and respiration rate within the approved error range by the American Thoracic Society (ATS). Further, we show that our approach is robust to sensor placement inside the mask.Comment: Accepted in the ACM Transactions on Computing for Healthcare (HEALTH