Latent Bayesian melding for integrating individual and population models

In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour. This raises the question of how to integrate both types of models. Methods such as posterior regularization follow the idea of generalized moment matching, in that they allow matching expectations between two models, but sometimes both models are most conveniently expressed as latent variable models. We propose latent Bayesian melding, which is motivated by averaging the distributions over populations statistics of both the individual-level and the population-level models under a logarithmic opinion pool framework. ln a case study on electricity disaggregation, which is a type of single channel blind source separation problem, we show that latent Bayesian melding leads to significantly more accurate predictions than an approach based solely on generalized moment matching

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

Non-Intrusive Load Monitoring in the OpenHAB Smart Home Framework

Non-intrusive load monitoring (NILM) on elektrienergia tarbimise jälgimise meetod, milles rakendatakse masinõppe meetodeid, et automaatselt vooluvõrku ühendatud seadmete kogutarbest eraldada üksikute seadmete energiatarve. Käesolev bakalaureusetöö kirjeldab reaalajas töötavat NILM lahendust, mis saab sisendina kasutatavad energiatarbimise andmed targa kodu automatiseerimise keskkonnast openHAB. Kogu energiatarbimise disagregeerimiseks kasutatakse vabavaralist tööriista NILMTK, mis võimaldab kogu süsteemi energiatarbimisest üksikute seadmete tarbimist eraldada. Tuvastatud seadmete hetketarbimised saadetakse tagasi openHABi keskkonda, kus neid võib kasutada koduautomaatikas. Arvutiteaduse instituudi värkvõrgu laboris läbi viidud testide tulemused näitavad, et lahendus suudab täpselt tuvastada stabiilse ja suure energiatarbimisega seadmeid (näiteks soojapuhur), kuid on ebatäpne selliste seadmete eristamisel, mille energiatarbimine kõigub palju (näiteks kohvimasin) või moodustab kogutarbimisest vaid väikse osa (näiteks pirn).Non-intrusive load monitoring (NILM) is an approach to energy monitoring, where machine learning techniques are applied to data from a single energy meter to determine the energy consumption of each appliance connected to the local electric network. This thesis presents a real-time NILM solution for the smart home that relies on the home automation platform openHAB for live power readings. NILMTK – an open-source energy disaggregation toolkit – is used to break down the aggregate live energy consumption data to the appliance level in real time. The disaggregated power data are then sent back to openHAB, where they can be displayed to the user and enable further automation. Tests conducted at the University of Tartu Internet of Things and Smart Solutions laboratory indicate high recognition accuracy for appliances with a steady high energy demand (e.g. a space heater), while lower accuracy scores were reported for appliances with a fluctuating power demand (e.g. a coffee maker) and lowpowered appliances that only make up a small proportion of the total energy consumption (e.g. a light bulb)