Electricity consumption pattern disaggregation using non-intrusive appliance load monitoring method

In practice, a standard energy meter can only capture the overall electricity consumption and estimating electricity consumption pattern of various appliances from the overall consumption pattern is complicated. Therefore, the Non-Intrusive Appliance Load Monitoring (NIALM) technique can be applied to trace electricity consumption from each appliance in a monitored building. However, the method requires a detailed, second-by-second power consumption data which is commonly not available without the use of high specification energy meter. Hence, this paper analyzes the impact of different time sampling data in estimating the energy consumption pattern of various appliances through NIALM method. This is so that consumers will have an overview of time sampling data which is required in order to apply the NIALM technique. As for the analysis, air-conditioning systems and fluorescent lamps were used in the experimental setup. One minute sample rate was the minimum time interval required by NIALM carried out in this analysis. Through the study presented in this paper, it can be established that higher time sampling led to uncertain appliance detection and low accuracy

Annotated Bibliography for the MATADOR Project

The MATADOR project is focused on developing methods to infer the operational mode of facilities that have the potential to be used in weapons development programs. Our central hypothesis is that by persistent, non-intrusive monitoring of such facilities, differences between various use scenarios can be reliably discovered. The impact of success in this area is that new tools and techniques for monitoring and treaty verification would make it easier to reliably discover and document weapons development activities. This document captures the literature that will serve as a basis to approach this task. The relevant literature is divided into topical areas that relate to the various aspects of expected MATADOR project development. We have found that very little work that is directly applicable for our purposes has been published, which has motivated the development of novel methods under the project. Therefore, the manuscripts referenced in this document were selected based on their potential use as foundational blocks for the methods we anticipate developing, or so that we can understand the limitations of existing methods

Recent approaches and applications of non-intrusive load monitoring

The Appliance Load Monitoring is vital in every energy consuming system be it commercial, residential or industrial in nature. Traditional load monitoring system, which used to be intrusive in nature require the installation of sensors to every load of interest which makes the system to be costly, time consuming and complex. Nonintrusive load monitoring (NILM) system uses the aggregated measurement at the utility service entry to identify and disaggregate the appliances connected in the building, which means only one set of sensors is required and it does not require entrance into the consumer premises. We presented a study in this paper providing a comprehensive review of the state of art of NILM, the different methods applied by researchers so far, before concluding with the future research direction, which include automatic home energy saving using NILM. The study also found that more efforts are needed from the researchers to apply NILM in appliance energy management, for example a Home Energy Management System (HEMS)

Feature Identification for Non-Intrusively Extracting Occupant Energy-Use Information in Office Buildings

Detailed energy-use information of office buildings’ occupants is necessary to implement proper simulation/intervention techniques. However, acquiring accurate occupant-specific energy consumption in office buildings at low cost is currently a challenging task since existing intrusive load monitoring (ILM) technologies require a large capital investment to provide high-resolution electricity usage data for individual occupants. On the other hand, non-intrusive load monitoring (NILM) approaches have been proven as more cost effective and flexible approaches to provide energy-use information of individual appliances. Therefore, extending the concept of NILM to individual occupants would be beneficial. This paper proposes two occupancy-related energy-consuming features, delay interval and magnitude of power changes and evaluates their significances for extracting occupant-specific power changes in a non-intrusive manner. The proposed features were examined through implementing a logistic regression model as a predictor on aggregate energy load data collected from an office building. Hypotheses tests also confirmed that both features are statistically significant to non-intrusively derive individual occupants’ energy-use information. As the main contribution of this study, these features could be utilized in developing sophisticated NILM-based approaches to monitor individual occupant energy-consuming behavior. 

Load Characterization and Low-order Approximation for Smart Metering Data in the Spectral Domain

Smart metering data are providing new opportunities for various energy analyses at household level. However traditional load analyses based on time-series techniques are challenged due to the irregular patterns and large volume from smart metering data. This paper proposes a promising alternative to decompose smart metering data in the spectral domain, where i) the irregular load profiles can be characterized by the underlying spectral components, and ii) massive amount of load data can be represented by a small number of coefficients extracted from spectral components. This paper assesses the performances of load characterization at different aggregated levels by two spectral analysis techniques, using the discrete Fourier transform (DFT) and discrete wavelet transform (DWT). Results show that DWT significantly outperforms DFT for individual smart metering data while DFT could be effective at a highly aggregated level

Comprehensive feature selection for appliance classification in NILM

Since the inception of non-intrusive appliance load monitoring (NILM), extensive research has focused on identifying an effective set of features that allows to form a unique appliance signature to discriminate various loads. Although an abundance of features are reported in literature, most works use only a limited subset of them. A systematic comparison and combination of the available features in terms of their effectiveness is still missing. This paper, as its first contribution, offers a concise and updated review of the features reported in literature for the purpose of load identification. As a second contribution, a systematic feature elimination process is proposed to identify the most effective feature set. The analysis is validated on a large benchmark dataset and shows that the proposed feature elimination process improves the appliance classification accuracy for all the appliances in the dataset compared to using all the features or randomly chosen subsets of features. (C) 2017 Elsevier B.V. All rights reserved

Sistema de Monitorização não Intrusiva de Cargas

Devido ao crescimento do consumo energético a nível mundial, surgem preocupações relacionadas com as dificuldades de abastecimento, o esgotamento dos recursos energéticos e os impactos ambientais (alterações na camada de ozono, aquecimento global e outras alterações climáticas, etc.). A eficiência energética e o desenvolvimento de estratégias alternativas, como a aposta nas energias renováveis, a promoção de campanhas de eficiência energética e a monitorização de cargas elétricas, revelam-se parte da solução deste problema, mantendo um certo nível de conforto associado a uma redução de consumo e custos. A eficiência energética nos edifícios pode ser aumentada através da mudança do comportamento dos seus utilizadores. O desenvolvimento sistemas capazes de extrair informações detalhadas de consumo, períodos de funcionamento dos dispositivos, custos e emissões de dióxido de carbono, poderá consciencializar os utilizadores por forma a alterarem o seu comportamento. Partindo do cenário acima descrito, a presente dissertação apresenta o desenvolvimento de uma nova metodologia para identificação desagregada de dispositivos, bem como um sistema de monitorização não intrusiva de cargas, denominado por Monitor de Cargas Elétricas (MCE). O sistema desenvolvido apresenta a capacidade de desagregação de múltiplos dispositivos em funcionamento, sendo esta uma das contribuições originais do trabalho. Através do MCE, o utilizador tem acesso a várias informações acerca dos eventos ocorridos, isto é, dispositivos elétricos ligados e desligados, períodos de funcionamento, custos e emissões de 2. O algoritmo de desagregação de cargas desenvolvido e implementado no MCE, utiliza a Transformada de Fourier do sinal da corrente elétrica global como assinatura elétrica em combinação com um algoritmo genético. Ao longo das várias experiências efetuadas, o MCE apresentou uma taxa de sucesso global de 92% de reconhecimento dos dispositivos

Load Monitoring and fault detection on DC micro grids using STFT based feature vectors

This thesis explores load monitoring on dc micro-grids specifically applied to Naval shipboard power systems. More electronic loads are being placed on Naval ships and, increasingly, more of these loads are pulsed-power in nature; drawing pulsating current from the grid. This presents a challenge for conventional fault monitoring devices as many fault currents are also pulsating in nature and can be difficult to differentiate from a desirable pulse event. Short-time Fourier transform is the technique preferred in this work for spectral analysis of the current signals. In addition to event based monitoring, another unsupervised control is being employed to continuously check the frequency content of the current to look for arcing faults caused by loose electrical connections. The objective of the dissertation is to develop a load monitoring algorithm that records the current drawn by these loads and is able to detect events and differentiate between desirable events and faults using their frequency content in run-time. The algorithm is realized on an micro-controller unit and validated on a low-voltage dc test-bed