☆ 4.7 Article

An innovative deep anomaly detection of building energy consumption using energy time-series images

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2023)

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Dynamic hybrid mechanism-based differential evolution algorithm and its application

Yingjie Song et al.

Summary: This paper proposes an optimization algorithm QGDECC, which combines quantum evolutionary algorithm and genetic algorithm, to effectively schedule railway train delay. The algorithm is validated on benchmark functions and actual train operation data, and the results show that QGDECC has higher adaptability and convergence speed, and can effectively eliminate the impact of delay on the railway network.

EXPERT SYSTEMS WITH APPLICATIONS (2023)

Add to Collection
Article Energy & Fuels

Smart fusion of sensor data and human feedback for personalized energy-saving recommendations

Iraklis Varlamis et al.

Summary: This article presents an online recommender system that fuses sensor data with user habits and feedback to provide personalized energy-saving recommendations at the right moment for users to take energy-saving actions. The system continuously evaluates sensor data, user habits, and feedback to identify micro-moments that maximize the need for recommended action, thus increasing recommendation acceptance. The proposed system, based on stacked-LSTM for fusing multi-sensor data streams, achieved an accuracy range of 93% to 97% in predicting the right moment to send recommendations to users in various scenarios.

APPLIED ENERGY (2022)

Add to Collection
Article Energy & Fuels

Dynamic real-time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty

Sihua Yin et al.

Summary: This study proposes two unsupervised anomaly detection methods suitable for dynamic real-time abnormal energy consumption detection and verifies their ability to quickly and accurately detect anomalies through experiments. In addition, an energy efficiency optimization framework based on anomaly detection is presented, providing an effective solution for clean production and low carbon manufacturing of high energy consumption machines.

APPLIED ENERGY (2022)

Add to Collection
Article Computer Science, Information Systems

An explainable and efficient deep learning framework for video anomaly detection

Chongke Wu et al.

Summary: Deep learning-based video anomaly detection methods are effective but often require large-scale training datasets. This paper introduces an efficient deep learning framework that utilizes pre-trained models and combines autoencoder and SHAP for model interpretability.

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS (2022)

Add to Collection
Article Computer Science, Information Systems

Deep Autoencoder-Based Anomaly Detection of Electricity Theft Cyberattacks in Smart Grids

Abdulrahman Takiddin et al.

Summary: This article proposes the design of an electricity theft cyberattack detector for advanced metering infrastructures using deep autoencoders with a LSTM-based seq2seq structure. The use of seq2seq structures improves the detection performance compared to existing detectors, with an enhancement of 4-21% in detection rate and 4-13% in false alarm rate.

IEEE SYSTEMS JOURNAL (2022)

Add to Collection
Article Computer Science, Artificial Intelligence

Deep anomaly detection with self-supervised learning and adversarial training

Xianchao Zhang et al.

Summary: The study introduces a method called Deep Adversarial Anomaly Detection (DAAD) to overcome the limitations of existing anomaly detection methods. By modeling the marginal distributions of normal data and utilizing adversarial training, the performance of anomaly detection is improved. Reliable results are obtained through a majority voting strategy.

PATTERN RECOGNITION (2022)

Add to Collection
Article Nuclear Science & Technology

Research on anomaly detection method of nuclear power plant operation state based on unsupervised deep generative model

Xiangyu Li et al.

Summary: This paper proposes an abnormal operation state detection method of nuclear power plant based on unsupervised deep generative model. The method uses Variational Auto Encoders (VAE) for data preprocessing and Isolation Forest (iForest) to identify abnormal operation data. The verification results show that the method can distinguish the normal or abnormal operation state of the nuclear power plant in real time and provide judgment basis for accident classification and subsequent rescue.

ANNALS OF NUCLEAR ENERGY (2022)

Add to Collection
Article Computer Science, Artificial Intelligence

Variational autoencoders for anomaly detection in the behaviour of the elderly using electricity consumption data

Daniel Gonzalez et al.

Summary: According to the World Health Organization, the global population over 60 is expected to double by 2050, with a majority preferring to live alone. It is important to find mechanisms and tools, such as smart home systems, to help improve their autonomy.

EXPERT SYSTEMS (2022)

Add to Collection
Article Computer Science, Information Systems

IADF-CPS: Intelligent Anomaly Detection Framework towards Cyber Physical Systems

Senthil Murugan Nagarajan et al.

Summary: This research proposes an anomaly detection approach by integrating Convolutional Neural Network (CNN) and Kalman Filter (KF)-based Gaussian Mixture Model (GMM) for identifying and detecting anomalous behavior in Cyber-Physical Systems (CPSs). The approach improves detection accuracy through data preprocessing and model integration.

COMPUTER COMMUNICATIONS (2022)

Add to Collection
Article Computer Science, Artificial Intelligence

Deep anomaly detection in hyperspectral images based on membership maps and object area filtering

Mahdi Yousefan et al.

Summary: This paper proposes a novel framework for transferred deep learning-based anomaly detection in hyperspectral images, which includes PCA dimension reduction, CNN training, and OAF algorithm utilization. The framework outperforms many state-of-the-art methods and demonstrates excellent domain adaptability.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

Add to Collection
Article Engineering, Electrical & Electronic

Incipient Residual-Based Anomaly Detection in Power Electronic Devices

Qian Yang et al.

Summary: This article proposes two anomaly detection methods for monitoring the condition of power electronics components in electric vehicles. The methods involve data preprocessing, multivariate regression modeling, and sequential analysis of residuals. The proposed methods utilize median filtering and feature selection to represent the nominal circuit behavior and generate residuals for anomaly detection. The methods are validated on accelerated aging experimental datasets and can potentially be embedded in real-time digital platforms.

IEEE TRANSACTIONS ON POWER ELECTRONICS (2022)

Add to Collection
Review Computer Science, Artificial Intelligence

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Yassine Himeur et al.

Summary: This paper provides a comprehensive review of recent trends in smart nonintrusive load monitoring (NILM) and proposes a multiperspective classification of existing techniques. Attention is given to the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Technical aspects, including data collection devices and public data sets, are investigated. Event-based and non-event-based NILM algorithms are overviewed. Potential limitations of existing solutions, such as security and privacy preservation issues, data scarcity, and implementation challenges, are identified. Future directions to overcome these limitations are explored.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2022)

Add to Collection
Article Biophysics

A deep residual inception network with channel attention modules for multi-label cardiac abnormality detection from reduced-lead ECG

Apoorva Srivastava et al.

Summary: This study proposes a method for cardiac arrhythmia classification using a deep residual inception network with a channel attention mechanism. Experimental evaluation on a large dataset demonstrates the effectiveness of the proposed model, achieving accurate diagnosis of twenty-nine cardiac abnormalities and normal electrocardiograms. The significance of this study lies in the development of a more robust and interpretable model, with potential applications in clinical diagnosis.

PHYSIOLOGICAL MEASUREMENT (2022)

Add to Collection
Article Construction & Building Technology

An innovative edge-based Internet of Energy solution for promoting energy saving in buildings

Abdullah Alsalemi et al.

Summary: Due to the popularity and maturity of AI, Edge Computing has become a popular alternative to cloud platforms in IoE and IoT applications. This paper proposes a novel Edge IoE system called M2SP-EdgeIoE, which incorporates various components such as sensing unit, energy disaggregation unit, anomaly detection platform, and recommender system unit to enable potential IoE use scenarios for domestic energy efficiency applications. The system has achieved promising performance in several scenarios.

SUSTAINABLE CITIES AND SOCIETY (2022)

Add to Collection
Article Energy & Fuels

Neural network-based model predictive control system for optimizing building automation and management systems of sports facilities

Mariam Elnour et al.

Summary: This study proposes a neural network-based model predictive control system for managing and optimizing the HVAC system of a sports hall. The neural network model outperforms other machine learning models and achieves significant energy reduction while improving indoor environmental quality.

APPLIED ENERGY (2022)

Add to Collection
Article Engineering, Electrical & Electronic

Cascading Synchronization Instability in Multi-VSC Grid-Connected System

Xikun Fu et al.

Summary: In this study, a cascading synchronization instability phenomenon in a multi-VSC grid is reported. It is found that both static interaction and dynamic interaction can affect the synchronization stability during the fault transient. Simulation and experimental results confirm the analysis.

IEEE TRANSACTIONS ON POWER ELECTRONICS (2022)

Add to Collection
Review Automation & Control Systems

Deep and transfer learning for building occupancy detection: A review and comparative analysis

Aya Nabil Sayed et al.

Summary: The building internet of things (BIoT) is a promising concept for reducing energy consumption, cutting costs, and promoting building transformation. Integrating artificial intelligence (AI) into the BIoT is crucial for data analysis and intelligent decision-making. This article provides an in-depth survey of strategies used to analyze sensor data and determine building occupancy, with a focus on deep learning and transfer learning approaches. Privacy and precision concerns in the current occupancy detection system are thoroughly discussed. Various directions are proposed to address privacy issues and improve detection accuracy.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2022)

Add to Collection
Article Computer Science, Information Systems

Multi-strategy particle swarm and ant colony hybrid optimization for airport taxiway planning problem

Wu Deng et al.

Summary: The study proposed a multi-strategy particle swarm and ant colony hybrid optimization algorithm (MPSACO) to solve airport taxiway planning problems, aiming to enhance resource utilization efficiency and reduce conflicts through optimization algorithms.

INFORMATION SCIENCES (2022)

Add to Collection
Article Green & Sustainable Science & Technology

Interactive visual study for residential energy consumption data

Ayman Al-Kababji et al.

Summary: This paper evaluates six energy data visualization tools to determine the optimal tool. The feedback collected through a questionnaire survey shows that conventional visualizations scored higher in terms of understandability, effectiveness, and aesthetic, while enhanced charts performed better in generating correct responses to analysis questions.

JOURNAL OF CLEANER PRODUCTION (2022)

Add to Collection
Article Construction & Building Technology

Next-generation energy systems for sustainable smart cities: Roles of transfer learning

Yassine Himeur et al.

Summary: Smart cities aim to reduce wasted energy, improve grid stability, and meet service demand by adopting next-generation energy systems and leveraging artificial intelligence, IoT, and communication technologies to collect and analyze real-time big data. However, training machine learning algorithms for energy-related tasks in sustainable smart cities is a challenging data science task. Transfer learning has been proposed as a promising solution to address these challenges.

SUSTAINABLE CITIES AND SOCIETY (2022)

Add to Collection
Article Environmental Studies

Economic performance and natural resources: Evaluating the role of economic risk

Wei Deng et al.

Summary: This study investigates the association between natural resources and economic performance in the BRICS countries over the period of 1990-2020, and finds that natural resources are a blessing for these economies, while renewable energy consumption and technological innovation contribute significantly to economic growth.

RESOURCES POLICY (2022)

Add to Collection
Article Computer Science, Artificial Intelligence

Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem

Wu Deng et al.

Summary: The paper proposes a novel improved DE algorithm WMSDE, which combines wavelet basis function to optimize mutation strategy and control parameters, accelerate convergence, improve search quality, and address issues like local optima and stagnation. Experimental results demonstrate the superior performance of WMSDE on 11 benchmark functions, as well as its effectiveness in solving real-world airport gate assignment problems.

APPLIED SOFT COMPUTING (2021)

Add to Collection
Article Energy & Fuels

A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings

Roberto Chiosa et al.

Summary: This study proposes an innovative anomaly detection and diagnosis method to automatically detect anomalous energy consumption at the whole-building meter level and diagnose the sub-loads responsible for the anomalies.

ENERGIES (2021)

Add to Collection
Article Computer Science, Software Engineering

Realtime and Accurate 3D Eye Gaze Capture with DCNN-Based Iris and Pupil Segmentation

Zhiyong Wang et al.

Summary: This paper proposes a realtime and accurate method for 3D eye gaze tracking using deep convolutional neural networks, which combines training efficiency and tracking accuracy in various conditions, advancing the state of the art in single RGB camera eye tracking.

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS (2021)

Add to Collection
Article Engineering, Electrical & Electronic

Smart Sensing and End-Users' Behavioral Change in Residential Buildings: An Edge-Based Internet of Energy Perspective

Abdullah Alsalemi et al.

Summary: The Internet of Energy is revolutionizing the building energy industry through innovations in data collection, interpretation, and behavioral improvement. Micro-moment analysis helps extract relevant energy consumption data, leading to behavior improvement through recommender systems.

IEEE SENSORS JOURNAL (2021)

Add to Collection
Article Energy & Fuels

Wind Turbine Gearbox Anomaly Detection Based on Adaptive Threshold and Twin Support Vector Machines

Harsh Dhiman et al.

Summary: Data-driven condition monitoring using adaptive threshold and TWSVM for wind turbine gearbox anomaly detection improves reliability and reduces downtime. By analyzing gearbox oil and bearing temperatures as time-series, the proposed method shows accurate performance compared to standard classifiers.

IEEE TRANSACTIONS ON ENERGY CONVERSION (2021)

Add to Collection
Review Computer Science, Theory & Methods

Deep Learning for Anomaly Detection: A Review

Guansong Pang et al.

Summary: Deep anomaly detection has emerged as a critical direction in the research field of anomaly detection, covering advancements in multiple categories of methods. Reviewing them can help us understand their advantages, disadvantages, and how they address challenges.

ACM COMPUTING SURVEYS (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

Smart power consumption abnormality detection in buildings using micromoments and improved K-nearest neighbors

Yassine Himeur et al.

Summary: Anomaly detection in energy consumption is crucial for developing efficient energy saving systems. Two novel schemes proposed in this paper show promising results in detecting abnormal consumption patterns in buildings. Empirical evaluation demonstrates the effectiveness of one scheme in achieving high accuracy and F1 score on real-world data.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2021)

Add to Collection
Article Construction & Building Technology

Application of data-driven attack detection framework for secure operation in smart buildings

Mariam Elnour et al.

Summary: This paper presents an analysis of the security of the HVAC system using a TRNSYS model and a data-driven attack detection strategy. Three schemes were investigated, including PCA feature extraction and 1D CNN-based encoder for temporal feature extraction, showing promising capabilities in attack detection with high reliability and low computational cost compared to standard machine-learning approaches.

SUSTAINABLE CITIES AND SOCIETY (2021)

Add to Collection
Review Energy & Fuels

Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives

Yassine Himeur et al.

Summary: This paper provides a comprehensive review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence, introducing a comprehensive taxonomy and discussing unresolved challenges and future research directions.

APPLIED ENERGY (2021)

Add to Collection
Article Thermodynamics

A data-driven approach to anomaly detection and vulnerability dynamic analysis for large-scale integrated energy systems

Li Zhang et al.

Summary: The study introduces a method to detect anomalies in an integrated energy system and analyze the system's vulnerability. By combining anomaly detection and complex network theory modeling, the approach effectively exposes security vulnerabilities in the system.

ENERGY CONVERSION AND MANAGEMENT (2021)

Add to Collection
Article Chemistry, Multidisciplinary

A Study of Features and Deep Neural Network Architectures and Hyper-Parameters for Domestic Audio Classification

Abigail Copiaco et al.

Summary: The algorithms explored in this research are applicable to multi-level classification applications, especially in audio classification. By employing transfer learning and simplifying network structures, it is possible to reduce time and resource requirements while maintaining classification accuracy.

APPLIED SCIENCES-BASEL (2021)

Add to Collection
Article Computer Science, Information Systems

A Nonintrusive Load Identification Model Based on Time-Frequency Features Fusion

Kexin Li et al.

Summary: This article proposes a two-stream convolutional neural network based on current time-frequency feature fusion for nonintrusive load identification, which outperforms state-of-the-art methods in experimental results on the PLAID and IDOUC datasets.

IEEE ACCESS (2021)

Add to Collection
Article Computer Science, Information Systems

A Cyber-Security Methodology for a Cyber-Physical Industrial Control System Testbed

Mohammad Noorizadeh et al.

Summary: This study proposes a cyber-physical industrial control system testbed for simulating real-time data generation and cyber attacks, and compares and evaluates various attack detection algorithms.

IEEE ACCESS (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

The emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency

Christos Sardianos et al.

Summary: Recent advances in artificial intelligence, especially in machine learning and deep learning, have improved the performance of intelligent systems and increased the demand for understanding system reasoning. Explainability in recommendation systems is essential to enhance user trust and acceptance.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2021)

Add to Collection
Article Thermodynamics

Abnormal energy consumption detection for GSHP system based on ensemble deep learning and statistical modeling method

Chengliang Xu et al.

INTERNATIONAL JOURNAL OF REFRIGERATION (2020)

Add to Collection
Article Construction & Building Technology

A hybrid data mining approach for anomaly detection and evaluation in residential buildings energy data

Chengliang Xu et al.

ENERGY AND BUILDINGS (2020)

Add to Collection
Review Chemistry, Multidisciplinary

Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

Ibrahem Kandel et al.

APPLIED SCIENCES-BASEL (2020)

Add to Collection
Article Energy & Fuels

Buildings Energy Efficiency Analysis and Classification Using Various Machine Learning Technique Classifiers

Cesar Benavente-Peces et al.

ENERGIES (2020)

Add to Collection
Article Engineering, Multidisciplinary

Classification of flower species by using features extracted from the intersection of feature selection methods in convolutional neural network models

Mesut Togacar et al.

MEASUREMENT (2020)

Add to Collection
Article Construction & Building Technology

Anomaly detection based on machine learning in IoT-based vertical plant wall for indoor climate control

Yu Liu et al.

BUILDING AND ENVIRONMENT (2020)

Add to Collection
Article Engineering, Electrical & Electronic

An Improved Quantum-Inspired Differential Evolution Algorithm for Deep Belief Network

Wu Deng et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

Add to Collection
Article Green & Sustainable Science & Technology

A deep learning approach for anomaly detection and prediction in power consumption data

C. Chahla et al.

ENERGY EFFICIENCY (2020)

Add to Collection
Article Computer Science, Artificial Intelligence

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

Yassine Himeur et al.

COGNITIVE COMPUTATION (2020)

Add to Collection
Review Construction & Building Technology

Building power consumption datasets: Survey, taxonomy and future directions

Yassine Himeur et al.

ENERGY AND BUILDINGS (2020)

Add to Collection
Article Computer Science, Information Systems

A Dual-Isolation-Forests-Based Attack Detection Framework for Industrial Control Systems

Mariam Elnour et al.

IEEE ACCESS (2020)

Add to Collection
Article Thermodynamics

Least squares support vector machine (LS-SVM)-based chiller fault diagnosis using fault indicative features

Hua Han et al.

APPLIED THERMAL ENGINEERING (2019)

Add to Collection
Article Construction & Building Technology

Novel real-time model-based fault detection method for automatic identification of abnormal energy performance in building ventilation units

Mads Bang et al.

ENERGY AND BUILDINGS (2019)

Add to Collection
Article Computer Science, Information Systems

Drift-Aware Methodology for Anomaly Detection in Smart Grid

Giuseppe Fenza et al.

IEEE ACCESS (2019)

Add to Collection
Article Computer Science, Information Systems

A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids

Hadis Karimipour et al.

IEEE ACCESS (2019)

Add to Collection
Article Energy & Fuels

Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data

Cheng Fan et al.

APPLIED ENERGY (2018)

Add to Collection
Article Thermodynamics

Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings

Alfonso Capozzoli et al.

ENERGY (2018)

Add to Collection
Article Engineering, Electrical & Electronic

An anomaly detection framework for identifying energy theft and defective meters in smart grids

Sook-Chin Yip et al.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2018)

Add to Collection
Article Computer Science, Artificial Intelligence

Energy-based anomaly detection for mixed data

Kien Do et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2018)

Add to Collection
Article Computer Science, Artificial Intelligence

Recent advances in convolutional neural networks

Jiuxiang Gu et al.

PATTERN RECOGNITION (2018)

Add to Collection
Article Construction & Building Technology

An ensemble learning framework for anomaly detection in building energy consumption

Daniel B. Araya et al.

ENERGY AND BUILDINGS (2017)

Add to Collection
Article Energy & Fuels

Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions

Guannan Li et al.

APPLIED ENERGY (2017)

Add to Collection
Article Computer Science, Hardware & Architecture

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky et al.

COMMUNICATIONS OF THE ACM (2017)

Add to Collection
Proceedings Paper Computer Science, Artificial Intelligence

Xception: Deep Learning with Depthwise Separable Convolutions

Francois Chollet

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Add to Collection
Article Geochemistry & Geophysics

Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

Yushi Chen et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2016)

Add to Collection
Article Construction & Building Technology

Temporal knowledge discovery in big BAS data for building energy management

Cheng Fan et al.

ENERGY AND BUILDINGS (2015)

Add to Collection
Proceedings Paper Computer Science, Theory & Methods

Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

Jack Kelly et al.

BUILDSYS'15 PROCEEDINGS OF THE 2ND ACM INTERNATIONAL CONFERENCE ON EMBEDDED SYSTEMS FOR ENERGY-EFFICIENT BUILT (2015)

Add to Collection
Webinars Posters Questions Hubs Funding Journals Papers Connect Collections Reviewers About Us FAQs Mobile App Privacy Policy Terms of Use
© Peeref 2019-2024. All rights reserved.