695 research outputs found
NILM techniques for intelligent home energy management and ambient assisted living: a review
The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora:
Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve
01/SAICT/2018/39578
Fundação para a Ciência e Tecnologia through IDMEC, under LAETA:
SFRH/BSAB/142998/2018
SFRH/BSAB/142997/2018
UID/EMS/50022/2019
Junta de Comunidades de Castilla-La-Mancha, Spain:
SBPLY/17/180501/000392
Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project):
TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio
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
An In Depth Study into Using EMI Signatures for Appliance Identification
Energy conservation is a key factor towards long term energy sustainability.
Real-time end user energy feedback, using disaggregated electric load
composition, can play a pivotal role in motivating consumers towards energy
conservation. Recent works have explored using high frequency conducted
electromagnetic interference (EMI) on power lines as a single point sensing
parameter for monitoring common home appliances. However, key questions
regarding the reliability and feasibility of using EMI signatures for
non-intrusive load monitoring over multiple appliances across different sensing
paradigms remain unanswered. This work presents some of the key challenges
towards using EMI as a unique and time invariant feature for load
disaggregation. In-depth empirical evaluations of a large number of appliances
in different sensing configurations are carried out, in both laboratory and
real world settings. Insights into the effects of external parameters such as
line impedance, background noise and appliance coupling on the EMI behavior of
an appliance are realized through simulations and measurements. A generic
approach for simulating the EMI behavior of an appliance that can then be used
to do a detailed analysis of real world phenomenology is presented. The
simulation approach is validated with EMI data from a router. Our EMI dataset -
High Frequency EMI Dataset (HFED) is also released
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
Energy Disaggregation Using Elastic Matching Algorithms
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.Peer reviewedFinal Published versio
Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting
This paper proposes a novel Non-Intrusive Load
Monitoring (NILM) method which incorporates appliance usage
patterns (AUPs) to improve performance of active load identi-
fication and forecasting. In the first stage, the AUPs of a given
residence were learnt using a spectral decomposition based standard
NILM algorithm. Then, learnt AUPs were utilized to bias
the priori probabilities of the appliances through a specifically
constructed fuzzy system. The AUPs contain likelihood measures
for each appliance to be active at the present instant based on
the recent activity/inactivity of appliances and the time of day.
Hence, the priori probabilities determined through the AUPs
increase the active load identification accuracy of the NILM
algorithm. The proposed method was successfully tested for
two standard databases containing real household measurements
in USA and Germany. The proposed method demonstrates an
improvement in active load estimation when applied to the
aforementioned databases as the proposed method augments the
smart meter readings with the behavioral trends obtained from
AUPs. Furthermore, a residential power consumption forecasting
mechanism, which can predict the total active power demand of
an aggregated set of houses, five minutes ahead of real time, was
successfully formulated and implemented utilizing the proposed
AUP based technique
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