Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors

A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.

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A novel two-stage method of plant seedlings classification based on deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211507 ◽

2021 ◽

pp. 1-11

Author(s):

Tianhong Dai ◽

Shijie Cong ◽

Jianping Huang ◽

Yanwen Zhang ◽

Xinwang Huang ◽

...

Keyword(s):

Deep Learning ◽

Learning Technology ◽

Two Stage ◽

Second Stage ◽

Stage Classification ◽

Different Types ◽

Two Stages ◽

Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

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A Two-Stage Machine Learning Classification Approach to Identify Extremism in Arabic Opinions

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/391022021 ◽

2021 ◽

Vol 10 (2) ◽

pp. 736-745

Keyword(s):

Machine Learning ◽

Binary Classification ◽

Feature Selection Method ◽

Support Vector ◽

Two Stage ◽

Machine Learning Classification ◽

Second Stage ◽

Testing Data ◽

Stage Classification ◽

Positive Dataset

The increased usage of the Internet and social networks allowed and enabled people to express their views, which have generated an increasing attention lately. Sentiment Analysis (SA) techniques are used to determine the polarity of information, either positive or negative, toward a given topic, including opinions. In this research, we have introduced a machine learning approach based on Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF) classifiers, to find and classify extreme opinions in Arabic reviews. To achieve this, a dataset of 1500 Arabic reviews was collected from Google Play Store. In addition, a two-stage Classification process was applied to classify the reviews. In the first stage, we built a binary classifier to sort out positive from negative reviews. In the second stage, however we applied a binary classification mechanism based on a set of proposed rules that distinguishes extreme positive from positive reviews, and extreme negative from negative reviews. Four major experiments were conducted with a total of 10 different sub experiments to fulfill the two-stage process using different X-validation schemas and Term Frequency-Inverse Document Frequency feature selection method. Obtained results have indicated that SVM was the best during the first stage classification with 30% testing data, and NB was the best with 20% testing data. The results of the second stage classification indicated that SVM has scored better results in identifying extreme positive reviews when dealing with the positive dataset with an overall accuracy of 68.7% and NB showed better accuracy results in identifying extreme negative reviews when dealing with the negative dataset, with an overall accuracy of 72.8%.

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Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature

Computational Intelligence and Neuroscience ◽

10.1155/2017/4216281 ◽

2017 ◽

Vol 2017 ◽

pp. 1-22 ◽

Cited By ~ 34

Author(s):

Jihyun Kim ◽

Thi-Thu-Huong Le ◽

Howon Kim

Keyword(s):

Deep Learning ◽

Power Consumption ◽

Distribution System ◽

Short Term Memory ◽

Electrical Power ◽

Electricity Consumption ◽

Classification Performance ◽

Robust Solution ◽

Proposed Model ◽

Load Monitoring

Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM’s issues and improve the performance of load identification.

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A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing

Energies ◽

10.3390/en13092195 ◽

2020 ◽

Vol 13 (9) ◽

pp. 2195

Author(s):

Hasan Rafiq ◽

Xiaohan Shi ◽

Hengxu Zhang ◽

Huimin Li ◽

Manesh Kumar Ochani

Keyword(s):

Neural Network ◽

Power Consumption ◽

Real Time ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Estimation Accuracy ◽

Post Processing ◽

Input Space ◽

Deep Recurrent Neural Network ◽

Load Monitoring

Non-intrusive load monitoring (NILM) is a process of estimating operational states and power consumption of individual appliances, which if implemented in real-time, can provide actionable feedback in terms of energy usage and personalized recommendations to consumers. Intelligent disaggregation algorithms such as deep neural networks can fulfill this objective if they possess high estimation accuracy and lowest generalization error. In order to achieve these two goals, this paper presents a disaggregation algorithm based on a deep recurrent neural network using multi-feature input space and post-processing. First, the mutual information method was used to select electrical parameters that had the most influence on the power consumption of each target appliance. Second, selected steady-state parameters based multi-feature input space (MFS) was used to train the 4-layered bidirectional long short-term memory (LSTM) model for each target appliance. Finally, a post-processing technique was used at the disaggregation stage to eliminate irrelevant predicted sequences, enhancing the classification and estimation accuracy of the algorithm. A comprehensive evaluation was conducted on 1-Hz sampled UKDALE and ECO datasets in a noised scenario with seen and unseen test cases. Performance evaluation showed that the MFS-LSTM algorithm is computationally efficient, scalable, and possesses better estimation accuracy in a noised scenario, and generalized to unseen loads as compared to benchmark algorithms. Presented results proved that the proposed algorithm fulfills practical application requirements and can be deployed in real-time.

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IMPEC: An Integrated System for Monitoring and Processing Electricity Consumption in Buildings

Sensors ◽

10.3390/s20041048 ◽

2020 ◽

Vol 20 (4) ◽

pp. 1048 ◽

Cited By ~ 3

Author(s):

Mohamed Aymane Ahajjam ◽

Daniel Bonilla Licea ◽

Mounir Ghogho ◽

Abdellatif Kobbane

Keyword(s):

Power Consumption ◽

Data Acquisition ◽

Electricity Consumption ◽

Integrated System ◽

Load Prediction ◽

Integrated Monitoring ◽

Prediction Algorithms ◽

Load Monitoring ◽

Industrial Settings ◽

Validation Tests

Non-intrusive Load Monitoring (NILM) systems aim at identifying and monitoring the power consumption of individual appliances using the aggregate electricity consumption. Many issues hinder their development. For example, due to the complexity of data acquisition and labeling, datasets are scarce; labeled datasets are essential for developing disaggregation and load prediction algorithms. In this paper, we introduce a new NILM system, called Integrated Monitoring and Processing Electricity Consumption (IMPEC). The main characteristics of the proposed system are flexibility, compactness, modularity, and advanced on-board processing capabilities. Both hardware and software parts of the system are described, along with several validation tests performed at residential and industrial settings.

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An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation

10.26686/wgtn.14676087.v1 ◽

2021 ◽

Author(s):

Mahdi Abdollahi ◽

Xiaoying Gao ◽

Yi Mei ◽

S Ghosh ◽

J Li

Keyword(s):

Classification Problem ◽

Substantial Improvement ◽

Two Stage ◽

Data Set ◽

Unified Medical Language System ◽

Domain Specific ◽

Second Stage ◽

Medical Text ◽

Medical Language ◽

Medical Documents

Document classification (DC) is the task of assigning pre-defined labels to unseen documents by utilizing a model trained on the available labeled documents. DC has attracted much attention in medical fields recently because many issues can be formulated as a classification problem. It can assist doctors in decision making and correct decisions can reduce the medical expenses. Medical documents have special attributes that distinguish them from other texts and make them difficult to analyze. For example, many acronyms and abbreviations, and short expressions make it more challenging to extract information. The classification accuracy of the current medical DC methods is not satisfactory. The goal of this work is to enhance the input feature sets of the DC method to improve the accuracy. To approach this goal, a novel two-stage approach is proposed. In the first stage, a domain-specific dictionary, namely the Unified Medical Language System (UMLS), is employed to extract the key features belonging to the most relevant concepts such as diseases or symptoms. In the second stage, PSO is applied to select more related features from the extracted features in the first stage. The performance of the proposed approach is evaluated on the 2010 Informatics for Integrating Biology and the Bedside (i2b2) data set which is a widely used medical text dataset. The experimental results show substantial improvement by the proposed method on the accuracy of classification.

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Off-Grid DoA Estimation via Two-Stage Cascaded Neural Network

Energies ◽

10.3390/en14010228 ◽

2021 ◽

Vol 14 (1) ◽

pp. 228

Author(s):

Hyeonjin Chung ◽

Hyeongwook Seo ◽

Jeungmin Joo ◽

Dongkeun Lee ◽

Sunwoo Kim

Keyword(s):

Neural Network ◽

Doa Estimation ◽

Activation Function ◽

Estimation Accuracy ◽

Accurate Estimation ◽

Two Stage ◽

Second Stage ◽

Simulation Results ◽

The Difference ◽

True Direction

This paper introduces an off-grid DoA estimation via two-stage cascaded network which can resolve a mismatch between true direction-of-arrival (DoA) and discrete angular grid. In the first-stage network, the initial DoAs are estimated with a convolutional neural network (CNN), where initial DoAs are mapped on the discrete angular grid. To deal with the mismatch between initially estimated DoAs and true DoAs, the second-stage network estimates a tuning vector which represents the difference between true DoAs and nearest discrete angles. By using tuning vector, the final DoAs are estimated by moving initially estimated DoAs as much as the difference between true DoAs and nearest discrete angles. The limitation on estimation accuracy induced by the discrete angular grid can be resolved with the proposed two-stage network so that the estimation accuracy can be further enhanced. Simulation results show that adding the second-stage network after the first-stage network helps improve the estimation accuracy by resolving mismatch induced by the discretized grid. In the aspect of the implementation of machine learning, results also show that using CNN and using PReLU as the activation function is the best option for accurate estimation.

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Weighted generalized estimating equations for response-adaptive treatment regimes in two-stage longitudinal studies

10.47302/jsr.2017510105 ◽

2017 ◽

Vol 51 (1) ◽

pp. 79-100

Author(s):

JESSE YENCHIH HSU ◽

ABDUS S. WAHED

Keyword(s):

Longitudinal Studies ◽

Generalized Estimating Equations ◽

Rating Scale ◽

Estimating Equations ◽

Repeated Measurements ◽

Two Stage ◽

Data Set ◽

Treatment Regimes ◽

Second Stage ◽

Generalized Estimating

Two-stage longitudinal studies are common in the treatment of mental diseases, such as chronic forms of major depressive disorders. Outcomes in such studies often consist of repeated measurements of scores, such as the 24-item Hamilton Rating Scale for Depression, throughout the duration of therapy. Two issues that make the analysis of data from such two-stage studies different from standard longitudinal data are: (1) the randomization in the second stage for patients who fail to respond in the first stage; and (2) the drop-out of patients which sometimes occurs before the second stage. In this article, we show how the weighted generalized estimating equations can be used to draw inference for treatment regimes from two-stage studies. Specifically, we show how to construct weights and use them in the generalized estimating equations to derive consistent estimators of regime effects, and compare them. Large-sample properties of the proposed estimators are derived analytically, and examined through simulations. We demonstrate our methods by applying them to a depression data set.

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Subtask Gated Networks for Non-Intrusive Load Monitoring

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33011150 ◽

2019 ◽

Vol 33 ◽

pp. 1150-1157 ◽

Cited By ~ 4

Author(s):

Changho Shin ◽

Sunghwan Joo ◽

Jaeryun Yim ◽

Hyoseop Lee ◽

Taesup Moon ◽

...

Keyword(s):

Electricity Consumption ◽

Seminal Paper ◽

Performance Improvements ◽

Energy Disaggregation ◽

Load Monitoring ◽

Significant Performance ◽

Standby Power ◽

Measurement Devices ◽

Classification Probability ◽

The Cost

Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household’s aggregate electricity consumption is broken down into electricity usages of individual appliances. In this way, the cost and trouble of installing many measurement devices over numerous household appliances can be avoided, and only one device needs to be installed. The problem has been well-known since Hart’s seminal paper in 1992, and recently significant performance improvements have been achieved by adopting deep networks. In this work, we focus on the idea that appliances have on/off states, and develop a deep network for further performance improvements. Specifically, we propose a subtask gated network that combines the main regression network with an on/off classification subtask network. Unlike typical multitask learning algorithms where multiple tasks simply share the network parameters to take advantage of the relevance among tasks, the subtask gated network multiply the main network’s regression output with the subtask’s classification probability. When standby-power is additionally learned, the proposed solution surpasses the state-of-the-art performance for most of the benchmark cases. The subtask gated network can be very effective for any problem that inherently has on/off states.

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MC-NILM: A Multi-Chain Disaggregation Method for NILM

Energies ◽

10.3390/en14144331 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4331

Author(s):

Hao Ma ◽

Juncheng Jia ◽

Xinhao Yang ◽

Weipeng Zhu ◽

Hong Zhang

Keyword(s):

Time Complexity ◽

Electricity Consumption ◽

High Time ◽

Research Focus ◽

Energy Disaggregation ◽

Load Monitoring

Non-intrusive load monitoring (NILM) is an approach that helps residents obtain detailed information about household electricity consumption and has gradually become a research focus in recent years. Most of the existing algorithms on NILM build energy disaggregation models independently for an individual appliance while neglecting the relation among them. For this situation, this article proposes a multi-chain disaggregation method for NILM (MC-NILM). MC-NILM integrates the models generated by existing algorithms and considers the relation among these models to improve the performance of energy disaggregation. Given the high time complexity of searching for the optimal MC-NILM structure, this article proposes two methods to reduce the time complexity, the k-length chain method and the graph-based chain generation method. Finally, we use the Dataport and UK-DALE datasets to evaluate the feasibility, effectiveness, and generality of the MC-NILM.

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