scholarly journals Adversarial Energy Disaggregation

2021 ◽  
Vol 2 (4) ◽  
pp. 1-16
Author(s):  
Zhekai Du ◽  
Jingjing Li ◽  
Lei Zhu ◽  
Ke Lu ◽  
Heng Tao Shen
Keyword(s):  
Energy Efficiency ◽  
Data Science ◽  
Adversarial Learning ◽  
Typical Application ◽  
Energy Disaggregation ◽  
Load Monitoring ◽  
Novel Method ◽  
Real World Datasets ◽  
Sustainable Energy Policy ◽  
Non Negative Matrix Factorization

Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data analysis. NILM aims to help households understand how the energy is used and consequently tell them how to effectively manage the energy, thus allowing energy efficiency, which is considered as one of the twin pillars of sustainable energy policy (i.e., energy efficiency and renewable energy). Although NILM is unidentifiable, it is widely believed that the NILM problem can be addressed by data science. Most of the existing approaches address the energy disaggregation problem by conventional techniques such as sparse coding, non-negative matrix factorization, and the hidden Markov model. Recent advances reveal that deep neural networks (DNNs) can get favorable performance for NILM since DNNs can inherently learn the discriminative signatures of the different appliances. In this article, we propose a novel method named adversarial energy disaggregation based on DNNs. We introduce the idea of adversarial learning into NILM, which is new for the energy disaggregation task. Our method trains a generator and multiple discriminators via an adversarial fashion. The proposed method not only learns shared representations for different appliances but captures the specific multimode structures of each appliance. Extensive experiments on real-world datasets verify that our method can achieve new state-of-the-art performance.

scholarly journals dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Instrusive Load Monitoring Datasets

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 123 ◽  
Author(s):  
Manuel Pereira ◽  
Nuno Velosa ◽  
Lucas Pereira
Keyword(s):  
Data Science ◽  
Vital Role ◽  
Technical Note ◽  
File Format ◽  
Structural Differences ◽  
Load Monitoring ◽  
Standard File Format ◽  
Real World Datasets ◽  
Development Evaluation ◽  
New Algorithms

Datasets play a vital role in data science and machine learning research as they serve as the basis for the development, evaluation, and benchmark of new algorithms. Non-Intrusive Load Monitoring is one of the fields that has been benefiting from the recent increase in the number of publicly available datasets. However, there is a lack of consensus concerning how dataset should be made available to the community, thus resulting in considerable structural differences between the publicly available datasets. This technical note presents the DSCleaner, a Python library to clean, preprocess, and convert time series datasets to a standard file format. Two application examples using real-world datasets are also presented to show the technical validity of the proposed library.

scholarly journals NILM-based approach for energy efficiency assessment of household appliances

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Fernando D. Garcia ◽  
Wesley A. Souza ◽  
Ivando S. Diniz ◽  
Fernando P. Marafão
Keyword(s):  
Energy Efficiency ◽  
Microscopic Analysis ◽  
Energy Usage ◽  
Household Appliances ◽  
Efficiency Assessment ◽  
Nearest Neighbours ◽  
Energy Disaggregation ◽  
Load Monitoring ◽  
The Individual ◽  
Operational Issues

Abstract This paper presents a novel Non-Intrusive Load Monitoring (NILM) approach focusing on the Energy Efficiency (EE) assessment of residential appliances. This approach (NILMEE) is able to identify the individual consumption of several household devices, providing proper information for evaluating energy efficiency and pointing out the operational issues or labelling mismatches of appliances, while recommending better practices for energy usage in specific consumer installations. The proposed approach was developed and evaluated by embedding the NILM engine on an electronic power meter, which performs a microscopic analysis on measured voltages and currents and provides the load disaggregation using the Conservative Power Theory for the feature extraction, K-Nearest Neighbours for the appliance classification, and the Power Signature Blob for the energy disaggregation. The disaggregation algorithm performance evaluation is carried out using NILMTK. Results show that NILM transcends the regular energy usage calculation, serving as a tool that enables the diagnosis of household appliances using the energy efficiency indexes provided by labels and standards.

scholarly journals Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Optimization Technique ◽  
Cost Effective ◽  
Optimization Method ◽  
Long Term Memory ◽  
Successful Implementation ◽  
Efficient Design ◽  
Energy Disaggregation ◽  
Load Monitoring

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

A novel method for analyzing the effect of dust accumulation on energy efficiency loss in photovoltaic (PV) system

Energy ◽  
2021 ◽  
pp. 121112
Author(s):  
Siyuan Fan ◽  
Yu Wang ◽  
Shengxian Cao ◽  
Tianyi Sun ◽  
Peng Liu
Keyword(s):  
Energy Efficiency ◽  
Pv System ◽  
Efficiency Loss ◽  
Novel Method ◽  
Dust Accumulation

scholarly journals Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

2019 ◽  
Vol 11 (11) ◽  
pp. 3222 ◽  
Author(s):  
Pascal Schirmer ◽  
Iosif Mporas
Keyword(s):  
Random Forest ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Regression ◽  
Nearest Neighbours ◽  
Energy Disaggregation ◽  
Vector Machines ◽  
Non Linear ◽  
Load Monitoring ◽  
Sinusoidal Current

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.

scholarly journals Label Distribution for Learning with Noisy Labels

2020 ◽  
Author(s):  
Yun-Peng Liu ◽  
Ning Xu ◽  
Yu Zhang ◽  
Xin Geng
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Confidence Estimation ◽  
Novel Method ◽  
Real World Datasets ◽  
Label Distribution ◽  
Noisy Labels

The performances of deep neural networks (DNNs) crucially rely on the quality of labeling. In some situations, labels are easily corrupted, and therefore some labels become noisy labels. Thus, designing algorithms that deal with noisy labels is of great importance for learning robust DNNs. However, it is difficult to distinguish between clean labels and noisy labels, which becomes the bottleneck of many methods. To address the problem, this paper proposes a novel method named Label Distribution based Confidence Estimation (LDCE). LDCE estimates the confidence of the observed labels based on label distribution. Then, the boundary between clean labels and noisy labels becomes clear according to confidence scores. To verify the effectiveness of the method, LDCE is combined with the existing learning algorithm to train robust DNNs. Experiments on both synthetic and real-world datasets substantiate the superiority of the proposed algorithm against state-of-the-art methods.

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