Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature

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.

Download Full-text

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327908666180727123657 ◽

2019 ◽

Vol 9 (1) ◽

pp. 16-31

Author(s):

Kyungkoo Jun

Keyword(s):

Fourier Transform ◽

Deep Learning ◽

Short Term Memory ◽

Window Size ◽

Sensor Data ◽

Data Sets ◽

Data Set ◽

Proposed Model ◽

Testing Data ◽

Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

Download Full-text

A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-2326 ◽

2021 ◽

pp. 182-189

Author(s):

S. Arokiaraj ◽

Dr. N. Viswanathan

Keyword(s):

Deep Learning ◽

Internet Of Things ◽

Short Term Memory ◽

The Other ◽

Learning Models ◽

Computational Overhead ◽

Temporal Features ◽

Human Movements ◽

Proposed Model ◽

Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

Download Full-text

Stock Pattern Classification from Charts using Deep Learning Algorithms

Academic Perspective Procedia ◽

10.33793/acperpro.03.01.89 ◽

2020 ◽

Vol 3 (1) ◽

pp. 445-454

Author(s):

Celal Buğra Kaya ◽

Alperen Yılmaz ◽

Gizem Nur Uzun ◽

Zeynep Hilal Kilimci

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Pattern Classification ◽

Short Term Memory ◽

Stock Exchange ◽

Learning Techniques ◽

Proposed Model ◽

Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

Download Full-text

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.

Download Full-text

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.

Download Full-text

Forecasting Day-Ahead Hourly Photovoltaic Power Generation Using Convolutional Self-Attention Based Long Short-Term Memory

Energies ◽

10.3390/en13154017 ◽

2020 ◽

Vol 13 (15) ◽

pp. 4017 ◽

Cited By ~ 2

Author(s):

Dukhwan Yu ◽

Wonik Choi ◽

Myoungsoo Kim ◽

Ling Liu

Keyword(s):

Power Generation ◽

Deep Learning ◽

Prediction Accuracy ◽

Short Term Memory ◽

Local Context ◽

Short Term ◽

Term Memory ◽

Proposed Model ◽

Long Short Term Memory ◽

Pv Power Generation

The problem of Photovoltaic (PV) power generation forecasting is becoming crucial as the penetration level of Distributed Energy Resources (DERs) increases in microgrids and Virtual Power Plants (VPPs). In order to improve the stability of power systems, a fair amount of research has been proposed for increasing prediction performance in practical environments through statistical, machine learning, deep learning, and hybrid approaches. Despite these efforts, the problem of forecasting PV power generation remains to be challenging in power system operations since existing methods show limited accuracy and thus are not sufficiently practical enough to be widely deployed. Many existing methods using long historical data suffer from the long-term dependency problem and are not able to produce high prediction accuracy due to their failure to fully utilize all features of long sequence inputs. To address this problem, we propose a deep learning-based PV power generation forecasting model called Convolutional Self-Attention based Long Short-Term Memory (LSTM). By using the convolutional self-attention mechanism, we can significantly improve prediction accuracy by capturing the local context of the data and generating keys and queries that fit the local context. To validate the applicability of the proposed model, we conduct extensive experiments on both PV power generation forecasting using a real world dataset and power consumption forecasting. The experimental results of power generation forecasting using the real world datasets show that the MAPEs of the proposed model are much lower, in fact by 7.7%, 6%, 3.9% compared to the Deep Neural Network (DNN), LSTM and LSTM with the canonical self-attention, respectively. As for power consumption forecasting, the proposed model exhibits 32%, 17% and 44% lower Mean Absolute Percentage Error (MAPE) than the DNN, LSTM and LSTM with the canonical self-attention, respectively.

Download Full-text

ECOVNet: a highly effective ensemble based deep learning model for detecting COVID-19

PeerJ Computer Science ◽

10.7717/peerj-cs.551 ◽

2021 ◽

Vol 7 ◽

pp. e551

Author(s):

Nihad Karim Chowdhury ◽

Muhammad Ashad Kabir ◽

Md. Muhtadir Rahman ◽

Noortaz Rezoana

Keyword(s):

Deep Learning ◽

Detection System ◽

Learning Model ◽

Classification Performance ◽

Data Sets ◽

X Rays ◽

Proposed Model ◽

Highly Effective ◽

Chest X Ray ◽

Deep Learning Model

The goal of this research is to develop and implement a highly effective deep learning model for detecting COVID-19. To achieve this goal, in this paper, we propose an ensemble of Convolutional Neural Network (CNN) based on EfficientNet, named ECOVNet, to detect COVID-19 from chest X-rays. To make the proposed model more robust, we have used one of the largest open-access chest X-ray data sets named COVIDx containing three classes—COVID-19, normal, and pneumonia. For feature extraction, we have applied an effective CNN structure, namely EfficientNet, with ImageNet pre-training weights. The generated features are transferred into custom fine-tuned top layers followed by a set of model snapshots. The predictions of the model snapshots (which are created during a single training) are consolidated through two ensemble strategies, i.e., hard ensemble and soft ensemble, to enhance classification performance. In addition, a visualization technique is incorporated to highlight areas that distinguish classes, thereby enhancing the understanding of primal components related to COVID-19. The results of our empirical evaluations show that the proposed ECOVNet model outperforms the state-of-the-art approaches and significantly improves detection performance with 100% recall for COVID-19 and overall accuracy of 96.07%. We believe that ECOVNet can enhance the detection of COVID-19 disease, and thus, underpin a fully automated and efficacious COVID-19 detection system.

Download Full-text

Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i6.pp5467-5476 ◽

2021 ◽

Vol 11 (6) ◽

pp. 5467

Author(s):

Surenthiran Krishnan ◽

Pritheega Magalingam ◽

Roslina Ibrahim

Keyword(s):

Neural Network ◽

Heart Disease ◽

Deep Learning ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Learning Model ◽

Disease Prediction ◽

The Neural Network ◽

Proposed Model ◽

Deep Learning Model

<span>This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.</span>

Download Full-text

A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique

Information ◽

10.3390/info12090374 ◽

2021 ◽

Vol 12 (9) ◽

pp. 374

Author(s):

Babacar Gaye ◽

Dezheng Zhang ◽

Aziguli Wulamu

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Deep Learning ◽

Sentiment Analysis ◽

Language Processing ◽

Short Term Memory ◽

State Of The Art ◽

Accuracy Score ◽

Learning Models ◽

Proposed Model

With the extensive availability of social media platforms, Twitter has become a significant tool for the acquisition of peoples’ views, opinions, attitudes, and emotions towards certain entities. Within this frame of reference, sentiment analysis of tweets has become one of the most fascinating research areas in the field of natural language processing. A variety of techniques have been devised for sentiment analysis, but there is still room for improvement where the accuracy and efficacy of the system are concerned. This study proposes a novel approach that exploits the advantages of the lexical dictionary, machine learning, and deep learning classifiers. We classified the tweets based on the sentiments extracted by TextBlob using a stacked ensemble of three long short-term memory (LSTM) as base classifiers and logistic regression (LR) as a meta classifier. The proposed model proved to be effective and time-saving since it does not require feature extraction, as LSTM extracts features without any human intervention. We also compared our proposed approach with conventional machine learning models such as logistic regression, AdaBoost, and random forest. We also included state-of-the-art deep learning models in comparison with the proposed model. Experiments were conducted on the sentiment140 dataset and were evaluated in terms of accuracy, precision, recall, and F1 Score. Empirical results showed that our proposed approach manifested state-of-the-art results by achieving an accuracy score of 99%.

Download Full-text

Deep Learning based Semantic Similarity Detection using Text Data

Information Technology And Control ◽

10.5755/j01.itc.49.4.27118 ◽

2020 ◽

Vol 49 (4) ◽

pp. 495-510

Author(s):

Muhammad Mansoor ◽

Zahoor ur Rehman ◽

Muhammad Shaheen ◽

Muhammad Attique Khan ◽

Mohamed Habib

Keyword(s):

Deep Learning ◽

Language Processing ◽

Short Term Memory ◽

Main Task ◽

Detection Algorithms ◽

Similarity Detection ◽

Novel Approach ◽

Proposed Model ◽

Memory Network ◽

Numeric Data

Similarity detection in the text is the main task for a number of Natural Language Processing (NLP) applications. As textual data is comparatively large in quantity and huge in volume than the numeric data, therefore measuring textual similarity is one of the important problems. Most of the similarity detection algorithms are based upon word to word matching, sentence/paragraph matching, and matching of the whole document. In this research, a novel approach is proposed using deep learning models, combining Long Short Term Memory network (LSTM) with Convolutional Neural Network (CNN) for measuring semantics similarity between two questions. The proposed model takes sentence pairs as input to measure the similarity between them. The model is tested on publicly available Quora’s dataset. The model in comparison to the existing techniques gave 87.50 % accuracy which is better than the previous approaches.

Download Full-text