2,200 research outputs found
Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation
In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.Peer reviewedFinal Published versio
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
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
Non-intrusive load monitoring of household devices using a hybrid deep learning model through convex hull-based data selection
The availability of smart meters and IoT technology has opened new opportunities, ranging from monitoring electrical energy to extracting various types of information related to household occupancy, and with the frequency of usage of different appliances. Non-intrusive load monitoring (NILM) allows users to disaggregate the usage of each device in the house using the total aggregated power signals collected from a smart meter that is typically installed in the household. It enables the monitoring of domestic appliance use without the need to install individual sensors for each device, thus minimizing electrical system complexities and associated costs. This paper proposes an NILM framework based on low frequency power data using a convex hull data selection approach and hybrid deep learning architecture. It employs a sliding window of aggregated active and reactive powers sampled at 1 Hz. A randomized approximation convex hull data selection approach performs the selection of the most informative vertices of the real convex hull. The hybrid deep learning architecture is composed of two models: a classification model based on a convolutional neural network trained with a regression model based on a bidirectional long-term memory neural network. The results obtained on the test dataset demonstrate the effectiveness of the proposed approach, achieving F1 values ranging from 0.95 to 0.99 for the four devices considered and estimation accuracy values between 0.88 and 0.98. These results compare favorably with the performance of existing approaches.This research was funded by Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020, grant numbers 39578/2018 and 72581/2020. Antonio Ruano also acknowledges the support of Fundação para a Ciência e Tecnologia, grant UID/EMS/50022/2020,
through IDMEC under LAETAinfo:eu-repo/semantics/publishedVersio
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
Activity Detection And Modeling Using Smart Meter Data: Concept And Case Studies
Electricity consumed by residential consumers counts for a significant part
of global electricity consumption and utility companies can collect
high-resolution load data thanks to the widely deployed advanced metering
infrastructure. There has been a growing research interest toward appliance
load disaggregation via nonintrusive load monitoring. As the electricity
consumption of appliances is directly associated with the activities of
consumers, this paper proposes a new and more effective approach, i.e.,
activity disaggregation. We present the concept of activity disaggregation and
discuss its advantage over traditional appliance load disaggregation. We
develop a framework by leverage machine learning for activity detection based
on residential load data and features. We show through numerical case studies
to demonstrate the effectiveness of the activity detection method and analyze
consumer behaviors by time-dependent activity modeling. Last but not least, we
discuss some potential use cases that can benefit from activity disaggregation
and some future research directions.Comment: 2020 IEEE Power & Energy Society General Meetin
GREEND: An Energy Consumption Dataset of Households in Italy and Austria
Home energy management systems can be used to monitor and optimize
consumption and local production from renewable energy. To assess solutions
before their deployment, researchers and designers of those systems demand for
energy consumption datasets. In this paper, we present the GREEND dataset,
containing detailed power usage information obtained through a measurement
campaign in households in Austria and Italy. We provide a description of
consumption scenarios and discuss design choices for the sensing
infrastructure. Finally, we benchmark the dataset with state-of-the-art
techniques in load disaggregation, occupancy detection and appliance usage
mining
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