41 research outputs found
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
A comparative study of low sampling non intrusive load dis-aggregation
International audienceNon-intrusive load monitoring (NILM) deals with the identification and subsequent energy estimation of the individual appliances from the smart meter data. The state of the art applications typically runs once per day and reports the detected appliances. In this work, data driven models are implemented for two different sampling rates (10 seconds and 15 minutes). The models are trained for 20 houses in the Netherlands and tested for a period of 4-weeks. The results indicate that the disaggregation methods is applicable for both sampling cases but with different use-case
Multi-timescale Event Detection in Nonintrusive Load Monitoring based on MDL Principle
Load event detection is the fundamental step for the event-based
non-intrusive load monitoring (NILM). However, existing event detection methods
with fixed parameters may fail in coping with the inherent multi-timescale
characteristics of events and their event detection accuracy is easily affected
by the load fluctuation. In this regard, this paper extends our previously
designed two-stage event detection framework, and proposes a novel
multi-timescale event detection method based on the principle of minimum
description length (MDL). Following the completion of step-like event detection
in the first stage, a long-transient event detection scheme with
variable-length sliding window is designed for the second stage, which is
intended to provide the observation and characterization of the same event at
different time scales. In that, the context information in the aggregated load
data is mined by motif discovery, and then based on the MDL principle, the
proper observation scales are selected for different events and the
corresponding detection results are determined. In the post-processing step, a
load fluctuation location method based on voice activity detection (VAD) is
proposed to identify and remove the unreasonable events caused by fluctuations.
Based on newly proposed evaluation metrics, the comparison tests on public and
private datasets demonstrate that our method achieves higher detection accuracy
and integrity for events of various appliances across different scenarios.Comment: 11 pages,16 figure
Non-intrusive Load Monitoring based on Self-supervised Learning
Deep learning models for non-intrusive load monitoring (NILM) tend to require
a large amount of labeled data for training. However, it is difficult to
generalize the trained models to unseen sites due to different load
characteristics and operating patterns of appliances between data sets. For
addressing such problems, self-supervised learning (SSL) is proposed in this
paper, where labeled appliance-level data from the target data set or house is
not required. Initially, only the aggregate power readings from target data set
are required to pre-train a general network via a self-supervised pretext task
to map aggregate power sequences to derived representatives. Then, supervised
downstream tasks are carried out for each appliance category to fine-tune the
pre-trained network, where the features learned in the pretext task are
transferred. Utilizing labeled source data sets enables the downstream tasks to
learn how each load is disaggregated, by mapping the aggregate to labels.
Finally, the fine-tuned network is applied to load disaggregation for the
target sites. For validation, multiple experimental cases are designed based on
three publicly accessible REDD, UK-DALE, and REFIT data sets. Besides,
state-of-the-art neural networks are employed to perform NILM task in the
experiments. Based on the NILM results in various cases, SSL generally
outperforms zero-shot learning in improving load disaggregation performance
without any sub-metering data from the target data sets.Comment: 12 pages,10 figure
VI-based appliance classification using aggregated power consumption data
Non-intrusive load monitoring detects active appliances in a household (and their power consumption) from measuring the aggregated power at just one point in that household. Our previous works focused on classifying a single appliance, assuming that the voltage and current trace could be isolated from an aggregated signal by considering the difference in current before and after the event. In this paper, we show that this assumption holds and that it is a viable approach in practice. We experimentally validate this for two classification methods we proposed earlier: (1) random forests using elliptical Fourier descriptors of the appliances' VI trajectories and (2) convolutional neural networks using the appliances' VI images. We benchmark these approaches on the aggregated data from the 2018 version of PLAID. We obtain, respectively for each of these classifiers, a maximal F-macro-measure of 85.31% and 87.95 %. We also show that using submetered data for training does not improve the performance
Time-frequency analysis techniques for non-intrusive load monitoring
The work
in this thesis examines time-frequency analysis techniques and in particular the wavelet
transform to extract the features contained within the electrical load signals. A novel approach
that is based on wavelet design was utilized to generate a wavelet library which was used to
match each load signal to a specific wavelet using Procrustes and covariance analysis. In order
to automate the load identification process, two machine learning classifiers representing an
eager learner and a lazy learner were used in this work. The proposed wavelet design concept
has been verified experimentally, and the results of implementing the proposed load detection
and classification approach shows significant improvement in the classification accuracy
compared to other existing detection approaches reaching an overall accuracy of 98%