scholarly journals A Framework to Generate and Label Datasets for Non-Intrusive Load Monitoring

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 75
Author(s):  
Benjamin Völker ◽  
Marc Pfeifer ◽  
Philipp M. Scholl ◽  
Bernd Becker
Keyword(s):  
Electricity Consumption ◽  
Ground Truth ◽  
Learning Approaches ◽  
Post Processing ◽  
Monitoring Methods ◽  
Ground Truth Data ◽  
Level Data ◽  
Consumption Data ◽  
Processing Step ◽  
Load Monitoring

In order to reduce the electricity consumption in our homes, a first step is to make the user aware of it. Raising such awareness, however, demands to pinpoint users of specific appliances that unnecessarily consume electricity. A retrofittable and scalable way to provide appliance-specific consumption is provided by Non-Intrusive Load Monitoring methods. These methods use a single electricity meter to record the aggregated consumption of all appliances and disaggregate it into the consumption of each individual appliance using advanced algorithms usually utilizing machine-learning approaches. Since these approaches are often supervised, labelled ground-truth data need to be collected in advance. Labeling on-phases of devices is already a tedious process, but, if further information about internal device states is required (e.g., intensity of an HVAC), manual post-processing quickly becomes infeasible. We propose a novel data collection and labeling framework for Non-Intrusive Load Monitoring. The framework is comprised of the hardware and software required to record and (semi-automatically) label the data. The hardware setup includes a smart-meter device to record aggregated consumption data and multiple socket meters to record appliance level data. Labeling is performed in a semi-automatic post-processing step guided by a graphical user interface, which reduced the labeling effort by 72% compared to a manual approach. We evaluated our framework and present the FIRED dataset. The dataset features uninterrupted, time synced aggregated, and individual device voltage and current waveforms with distinct state transition labels for a total of 101 days.

scholarly journals MORED: A Moroccan Buildings’ Electricity Consumption Dataset

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6737
Author(s):  
Mohamed Aymane Ahajjam ◽  
Daniel Bonilla Licea ◽  
Chaimaa Essayeh ◽  
Mounir Ghogho ◽  
Abdellatif Kobbane
Keyword(s):  
Electricity Consumption ◽  
Ground Truth ◽  
Ground Truth Data ◽  
Disadvantaged Neighborhoods ◽  
Consumption Data ◽  
Load Monitoring ◽  
Open Datasets

This paper consists of two parts: an overview of existing open datasets of electricity consumption and a description of the Moroccan Buildings’ Electricity Consumption Dataset, a first of its kind, coined as MORED. The new dataset comprises electricity consumption data of various Moroccan premises. Unlike existing datasets, MORED provides three main data components: whole premises (WP) electricity consumption, individual load (IL) ground-truth consumption, and fully labeled IL signatures, from affluent and disadvantaged neighborhoods. The WP consumption data were acquired at low rates (1/5 or 1/10 samples/s) from 12 households; the IL ground-truth data were acquired at similar rates from five households for extended durations; and IL signature data were acquired at high and low rates (50 k and 4 samples/s) from 37 different residential and industrial loads. In addition, the dataset encompasses non-intrusive load monitoring (NILM) metadata.

A Flying Gray Ball Multi-illuminant Image Dataset for Color Research

2020 ◽  
Vol 64 (5) ◽  
pp. 50411-1-50411-8
Author(s):  
Hoda Aghaei ◽  
Brian Funt
Keyword(s):  
Machine Vision ◽  
Spectral Reflectance ◽  
Color Constancy ◽  
Ground Truth ◽  
Practical Approach ◽  
Post Processing ◽  
Illumination Estimation ◽  
Processing Step ◽  
Image Dataset

Abstract For research in the field of illumination estimation and color constancy, there is a need for ground-truth measurement of the illumination color at many locations within multi-illuminant scenes. A practical approach to obtaining such ground-truth illumination data is presented here. The proposed method involves using a drone to carry a gray ball of known percent surface spectral reflectance throughout a scene while photographing it frequently during the flight using a calibrated camera. The captured images are then post-processed. In the post-processing step, machine vision techniques are used to detect the gray ball within each frame. The camera RGB of light reflected from the gray ball provides a measure of the illumination color at that location. In total, the dataset contains 30 scenes with 100 illumination measurements on average per scene. The dataset is available for download free of charge.

scholarly journals Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5236 ◽  
Author(s):  
Sanket Desai ◽  
Rabei Alhadad ◽  
Abdun Mahmood ◽  
Naveen Chilamkurti ◽  
Seungmin Rho
Keyword(s):  
Power Consumption ◽  
Large Scale ◽  
Single Point ◽  
Ground Truth ◽  
Significant Rise ◽  
The State ◽  
Smart Meters ◽  
Energy Estimation ◽  
Consumption Data ◽  
Load Monitoring

With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating the state and the power consumption of an individual appliance in a consumer’s premise using a single point of measurement device such as a smart meter. Although there are several existing NILM techniques, there is no meaningful and accurate metric to evaluate these NILM techniques for multi-state devices such as the fridge, heat pump, etc. In this paper, we demonstrate the inadequacy of the existing metrics and propose a new metric that combines both event classification and energy estimation of an operational state to give a more realistic and accurate evaluation of the performance of the existing NILM techniques. In particular, we use unsupervised clustering techniques to identify the operational states of the device from a labeled dataset to compute a penalty threshold for predictions that are too far away from the ground truth. Our work includes experimental evaluation of the state-of-the-art NILM techniques on widely used datasets of power consumption data measured in a real-world environment.

scholarly journals Training load monitoring algorithms on highly sub-metered home electricity consumption data

2008 ◽  
Vol 13 (S1) ◽  
pp. 406-411 ◽  
Author(s):  
Mario Berges ◽  
Ethan Goldman ◽  
H. Scott Matthews ◽  
Lucio Soibelman
Keyword(s):  
Electricity Consumption ◽  
Training Load ◽  
Consumption Data ◽  
Load Monitoring

scholarly journals Anomalies in the Sky: Experiments with traffic densities and airport runway use

10.29007/3lks ◽  
2019 ◽  
Author(s):  
Axel Tanner ◽  
Martin Strohmeier
Keyword(s):  
Anomaly Detection ◽  
Gini Index ◽  
Ground Truth ◽  
Air Traffic ◽  
Learning Approaches ◽  
Traffic Data ◽  
Traffic Patterns ◽  
Ground Truth Data ◽  
Political Events ◽  
Airport Runway

Anomalies in the airspace can provide an indicator of critical events and changes which go beyond aviation. Devising techniques, which can detect abnormal patterns can provide intelligence and information ranging from weather to political events. This work presents our latest findings in detecting such anomalies in air traffic patterns using ADS-B data provided by the OpenSky network [8]. After discussion of specific problems in anomaly detection in air traffic data, we show an experiment in a regional setting, evaluating air traffic densities with the Gini index, and a second experiment investigating the runway use at Zurich airport. In the latter case, strong available ground truth data allows to better understand and confirm findings of different learning approaches.

Long-Range Geo-Monitoring Using Image Assisted Total Stations

2014 ◽  
Vol 8 (3) ◽  
Author(s):  
Andreas Wagner ◽  
Ben Huber ◽  
Wolfgang Wiedemann ◽  
Gerhard Paar
Keyword(s):  
High Resolution ◽  
Long Range ◽  
Ground Truth ◽  
Monitoring Methods ◽  
Ground Truth Data ◽  
Areal Coverage ◽  
Achievable Accuracy ◽  
The Eu

AbstractImage Assisted Total Stations (IATS) unify geodetic precision of total stations with areal coverage of images. The concept of using two IATS devices for high-resolution, long-range stereo survey of georisk areas has been investigated in the EU-FP7 project DE-MONTES (www.de-montes.eu). The paper presents the used methodology and compares the main features with other terrestrial geodetic geo-monitoring methods. The theoretically achievable accuracy of the measurement systemis derived and verified by ground truth data of a distant clay pit slope and simulated deformations. It is shown that the stereo IATS concept is able to obtain higher precision in the determination of 3D deformations than other systems of comparable sensor establishment effort.

Lifestyle Analysis from Household Electricity Consumption Data

2020 ◽  
Vol 24 (2) ◽  
pp. 214-220
Author(s):  
Yu Shirai ◽  
Shunichi Hattori ◽  
Yasufumi Takama ◽  
◽  
Keyword(s):  
Quality Of Life ◽  
Electricity Consumption ◽  
Ground Truth ◽  
Smart Meters ◽  
Positive Correlation ◽  
Aging Society ◽  
Consumption Data ◽  
Target Data

This paper aims to analyze the lifestyle of residents from household electricity consumption data. Improving QOL (Quality of Life) of elderlies has attracted attention in a super-aging society. It is known that the lifestyle of a person directly affects his / her health and QOL. Therefore, understanding a lifestyle is expected to be useful for providing various support for improving QOL, such as recommending adequate actions and daily habit. As a means for understanding residents’ lifestyle, this paper focuses on household electricity consumption data, which gets to be available with the spread of smart meters. The analysis is conducted by estimating the time of taking essential actions such as wake up and eating. As the target data has no ground truth, this paper also shows the result of an experiment on the detection of the essential actions. The analysis results reveal several findings which could be useful for improving QOL, such as positive correlation between regularity of dinner time and bedtime.

scholarly journals Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Laura K. Young ◽  
Hannah E. Smithson
Keyword(s):  
Adaptive Optics ◽  
Retinal Image ◽  
Imaging System ◽  
Ground Truth ◽  
Learning Approaches ◽  
Image Capture ◽  
Ground Truth Data ◽  
Cone Mosaic ◽  
Synthetic Images

AbstractHigh resolution retinal imaging systems, such as adaptive optics scanning laser ophthalmoscopes (AOSLO), are increasingly being used for clinical research and fundamental studies in neuroscience. These systems offer unprecedented spatial and temporal resolution of retinal structures in vivo. However, a major challenge is the development of robust and automated methods for processing and analysing these images. We present ERICA (Emulated Retinal Image CApture), a simulation tool that generates realistic synthetic images of the human cone mosaic, mimicking images that would be captured by an AOSLO, with specified image quality and with corresponding ground-truth data. The simulation includes a self-organising mosaic of photoreceptors, the eye movements an observer might make during image capture, and data capture through a real system incorporating diffraction, residual optical aberrations and noise. The retinal photoreceptor mosaics generated by ERICA have a similar packing geometry to human retina, as determined by expert labelling of AOSLO images of real eyes. In the current implementation ERICA outputs convincingly realistic en face images of the cone photoreceptor mosaic but extensions to other imaging modalities and structures are also discussed. These images and associated ground-truth data can be used to develop, test and validate image processing and analysis algorithms or to train and validate machine learning approaches. The use of synthetic images has the advantage that neither access to an imaging system, nor to human participants is necessary for development.

scholarly journals Prediction and Optimization of Ion Transport Characteristics in Nanoparticle-Based Electrolytes Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Sanket Kadulkar ◽  
Michael Howard ◽  
Thomas Truskett ◽  
Venkat Ganesan
Keyword(s):  
Optimal Transport ◽  
Kinetic Monte Carlo ◽  
Ground Truth ◽  
Spatial Arrangement ◽  
Spherical Particles ◽  
Learning Approaches ◽  
Structure Property ◽  
Spatial Correlations ◽  
Ground Truth Data ◽  
Modeling Strategy

<pre>We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes, and use it to identify the characteristics of morphologies which exhibit optimal transport properties. The ground truth data is obtained from kinetic Monte Carlo (kMC) simulations of cation transport parameterized using a multiscale modeling strategy. We implement deep learning approaches to quantitatively link the diffusivity of cations to the spatial arrangement of the nanoparticles. We then integrate the trained CNN model with a topology optimization algorithm for accelerated discovery of nanoparticle morphologies that exhibit optimal cation diffusivities at a specified nanoparticle loading, and we investigate the ability of the CNN model to quantitatively account for the influence of interparticle spatial correlations on cation diffusivity. Finally, using data-driven approaches, we explore how simple descriptors of nanoparticle morphology correlate with cation diffusivity, thus providing a physical rationale for the observed optimal microstructures. The results of this study highlight the capability of CNNs to serve as surrogate models for structure--property relationships in composites with monodisperse spherical particles, which can in turn be used with inverse methods to discover morphologies that produce optimal target properties.</pre>

scholarly journals Creating a universal SNP and small indel variant caller with deep neural networks

2016 ◽  
Author(s):  
Ryan Poplin ◽  
Pi-Chuan Chang ◽  
David Alexander ◽  
Scott Schwartz ◽  
Thomas Colthurst ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Mammalian Species ◽  
Variant Calling ◽  
Ground Truth ◽  
Learning Approaches ◽  
Next Generation ◽  
Ground Truth Data ◽  
Sequencing Technologies ◽  
Significant Step ◽  
Generation Sequencing

AbstractNext-generation sequencing (NGS) is a rapidly evolving set of technologies that can be used to determine the sequence of an individual’s genome1 by calling genetic variants present in an individual using billions of short, errorful sequence reads2. Despite more than a decade of effort and thousands of dedicated researchers, the hand-crafted and parameterized statistical models used for variant calling still produce thousands of errors and missed variants in each genome3,4. Here we show that a deep convolutional neural network5 can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships (likelihoods) between images of read pileups around putative variant sites and ground-truth genotype calls. This approach, called DeepVariant, outperforms existing tools, even winning the “highest performance” award for SNPs in a FDA-administered variant calling challenge. The learned model generalizes across genome builds and even to other mammalian species, allowing non-human sequencing projects to benefit from the wealth of human ground truth data. We further show that, unlike existing tools which perform well on only a specific technology, DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, from deep whole genomes from 10X Genomics to Ion Ampliseq exomes. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.

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