Non-intrusive load monitoring based on low frequency active power measurements

A Non-Intrusive Load Monitoring (NILM) method for residential appliances based on ac- tive power signal is presented. This method works e ectively with a single active power measurement taken at a low sampling rate (1 s). The proposed method utilizes the Karhunen Lo ́ eve (KL) expan- sion to decompose windows of active power signals into subspace components in order to construct a unique set of features, referred to as signatures, from individual and aggregated active power signals. Similar signal windows were clustered in to one group prior to feature extraction. The clustering was performed using a modified mean shift algorithm. After the feature extraction, energy levels of signal windows and power levels of subspace components were utilized to reduce the number of possible ap- pliance combinations and their energy level combinations. Then, the turned on appliance combination and the energy contribution from individual appliances were determined through the Maximum a Pos- teriori (MAP) estimation. Finally, the proposed method was modified to adaptively accommodate the usage patterns of appliances at each residence. The proposed NILM method was validated using data from two public databases: tracebase and reference energy disaggregation data set (REDD). The pre- sented results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned on appliance combinations in real households. Furthermore, the results emphasise the importance of clustering and the integration of the usage behaviour pattern in the proposed NILM method for real household

Non-intrusive load monitoring under residential solar power influx

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method for a consumer premises with a residentially installed solar plant. This method simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels. Further, it works effectively with a single active power measurement taken at the total power entry point with a sampling rate of 1 Hz. First, a unique set of appliance and solar signatures were constructed using a high-resolution implementation of Karhunen Loéve expansion (KLE). Then, different operating modes of multi-state appliances were automatically classified utilizing a spectral clustering based method. Finally, using the total power demand profile, through a subspace component power level matching algorithm, the turned ON appliances along with their operating modes and power levels as well as the solar influx amount were found at each time point. The proposed NILM method was first successfully validated on six synthetically generated houses (with solar units) using real household data taken from the Reference Energy Disaggregation Dataset (REDD) - USA. Then, in order to demonstrate the scalability of the proposed NILM method, it was employed on a set of 400 individual households. From that, reliable estimations were obtained for the total residential solar generation and for the total load that can be shed to provide reserve services. Finally, through a developed prediction technique, NILM results observed from 400 households during four days in the recent past were utilized to predict the next day’s total load that can be shed

GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness

In recent hyperspectral unmixing (HU) literature, the application of deep learning (DL) has become more prominent, especially with the autoencoder (AE) architecture. We propose a split architecture and use a pseudo-ground truth for abundances to guide the `unmixing network' (UN) optimization. Preceding the UN, an `approximation network' (AN) is proposed, which will improve the association between the centre pixel and its neighbourhood. Hence, it will accentuate spatial correlation in the abundances as its output is the input to the UN and the reference for the `mixing network' (MN). In the Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness (GAUSS), we proposed using one-hot encoded abundances as the pseudo-ground truth to guide the UN; computed using the k-means algorithm to exclude the use of prior HU methods. Furthermore, we release the single-layer constraint on MN by introducing the UN generated abundances in contrast to the standard AE for HU. Secondly, we experimented with two modifications on the pre-trained network using the GAUSS method. In GAUSS$_\textit{blind}$, we have concatenated the UN and the MN to back-propagate the reconstruction error gradients to the encoder. Then, in the GAUSS$_\textit{prime}$, abundance results of a signal processing (SP) method with reliable abundance results were used as the pseudo-ground truth with the GAUSS architecture. According to quantitative and graphical results for four experimental datasets, the three architectures either transcended or equated the performance of existing HU algorithms from both DL and SP domains.Comment: 16 pages, 6 figure

Hands off : a handshake interaction detection and localization model for COVID-19 threat control

A handshake interaction localization model in real-time that may help mitigate the threat for transmitting COVID-19, is presented using computer vision in a non-intrusive technique. A real-time detection model (using YOLO/you only look once) is proposed to identify handshake interactions in realistic scenarios. YOLO can detect multiple interactions in a single frame. The model can be applied to public spaces to identify handshake interactions. The study is the first to use a human interaction localization model in a multi-person setting. YOLO is a convolutional neural network (CNN) for object detection in real-time.Lewis Power, Singapor

Holistic interpretation of public scenes using computer vision and temporal graphs to identify social distancing violations

Social distancing measures are proposed as the primary strategy to curb the spread of the COVID-19 pandemic. Therefore, identifying situations where these protocols are violated has implications for curtailing the spread of the disease and promoting a sustainable lifestyle. This paper proposes a novel computer vision-based system to analyze CCTV footage to provide a threat level assessment of COVID-19 spread. The system strives to holistically interpret the information in CCTV footage spanning multiple frames to recognize instances of various violations of social distancing protocols, across time and space, as well as identification of group behaviors. This functionality is achieved primarily by utilizing a temporal graph-based structure to represent the information of the CCTV footage and a strategy to holistically interpret the graph and quantify the threat level of the given scene. The individual components are evaluated in a range of scenarios, and the complete system is tested against human expert opinion. The results reflect the dependence of the threat level on people, their physical proximity, interactions, protective clothing, and group dynamics, with a system performance of 76% accuracy

Holistic interpretation of public scenes using computer vision and temporal graphs to identify social distancing violations

Social distancing measures are proposed as the primary strategy to curb the spread of the COVID-19 pandemic. Therefore, identifying situations where these protocols are violated has implications for curtailing the spread of the disease and promoting a sustainable lifestyle. This paper proposes a novel computer vision-based system to analyze CCTV footage to provide a threat level assessment of COVID-19 spread. The system strives to holistically interpret the information in CCTV footage spanning multiple frames to recognize instances of various violations of social distancing protocols, across time and space, as well as identification of group behaviors. This functionality is achieved primarily by utilizing a temporal graph-based structure to represent the information of the CCTV footage and a strategy to holistically interpret the graph and quantify the threat level of the given scene. The individual components are evaluated in a range of scenarios, and the complete system is tested against human expert opinion. The results reflect the dependence of the threat level on people, their physical proximity, interactions, protective clothing, and group dynamics, with a system performance of 76% accuracy

A sensitivity matrix approach using two-stage optimization for voltage regulation of LV networks with high PV penetration

The occurrence of voltage violations is a major deterrent for absorbing more rooftop solar power into smart Low-Voltage Distribution Grids (LVDGs). Recent studies have focused on decentralized control methods to solve this problem due to the high computational time in performing load flows in centralized control techniques. To address this issue, a novel sensitivity matrix was developed to estimate the voltages of the network by replacing load flow simulations. In this paper, a Centralized Active, Reactive Power Management System (CARPMS) is proposed to optimally utilize the reactive power capability of smart Photovoltaic (PV) inverters with minimal active power curtailment to mitigate the voltage violation problem. The developed sensitivity matrix is able to reduce the time consumed by 55.1% compared to load flow simulations, enabling near-real-time control optimization. Given the large solution space of power systems, a novel two-stage optimization is proposed, where the solution space is narrowed down by a Feasible Region Search (FRS) step, followed by Particle Swarm Optimization (PSO). The failure of standalone PSO to converge to a feasible solution for 34% of the scenarios evaluated further validates the necessity of the two-stage optimization using FRS. The performance of the proposed methodology was analysed in comparison to the load flow method to demonstrate the accuracy and the capability of the optimization algorithm to mitigate voltage violations in near-real time. The deviations of the mean voltages of the proposed methodology from the load flow method were: 6.5×10−3 p.u for reactive power control using Q-injection, 1.02×10−2 p.u for reactive power control using Q-absorption, and 0 p.u for active power curtailment case

IMU-based Modularized Wearable Device for Human Motion Classification

Human motion analysis is used in many different fields and applications. Currently, existing systems either focus on one single limb or one single class of movements. Many proposed systems are designed to be used in an indoor controlled environment and must possess good technical know-how to operate. To improve mobility, a less restrictive, modularized, and simple Inertial Measurement units based system is proposed that can be worn separately and combined. This allows the user to measure singular limb movements separately and also monitor whole body movements over a prolonged period at any given time while not restricted to a controlled environment. For proper analysis, data is conditioned and pre-processed through possible five stages namely power-based, clustering index-based, Kalman filtering, distance-measure-based, and PCA-based dimension reduction. Different combinations of the above stages are analyzed using machine learning algorithms for selected case studies namely hand gesture recognition and environment and shoe parameter-based walking pattern analysis to validate the performance capability of the proposed wearable device and multi-stage algorithms. The results of the case studies show that distance-measure-based and PCA-based dimension reduction will significantly improve human motion identification accuracy. This is further improved with the introduction of the Kalman filter. An LSTM neural network is proposed as an alternate classifier and the results indicate that it is a robust classifier for human motion recognition. As the results indicate, the proposed wearable device architecture and multi-stage algorithms are cable of distinguishing between subtle human limb movements making it a viable tool for human motion analysis.Comment: 10 pages, 12 figures, 28 reference