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Super Resolution Perception for Improving Data Completeness in Smart Grid State Estimation
2020
Engineering
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Society, and partially supported by Youth Innovation Promotion Association of Chinese Academy of Sciences. ...
Municipal Science and Technology Innovation Committee Basic Research project (JCYJ20170410172224515), partially supported by funding from Shenzhen Institute of Artificial Intelligence and Robotics for ...
: 1 d b b S S b (17) d d : l W W W W S (18)
Adaptive moment estimation The other disadvantage of the dominant gradient descent methods is that the current gradient is the only ...
doi:10.1016/j.eng.2020.06.006
fatcat:t7jtpbkb2bfbbjcij74ejbycl4
AMI-FML: A Privacy-Preserving Federated Machine Learning Framework for AMI
[article]
2021
arXiv
pre-print
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Machine learning (ML) based smart meter data analytics is very promising for energy management and demand-response applications in the advanced metering infrastructure(AMI). ...
The aggregator uses the learned model gradients received from the federated smart meters to generate an aggregate, robust RNN model which improves the forecasting accuracy for individual and aggregated ...
We used LSTM networks to accurately forecast energy consumption based on the historical smart meter readings. ...
arXiv:2109.05666v2
fatcat:3jciof2rmbfdvbkhxrkmo23ifi
Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid
2021
Energies
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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 ...
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 ...
Acknowledgments: The article was extracted from the PhD thesis prepared by Vahit Feryad under the supervision of ˙Ismail Hakkı Çavdar. ...
doi:10.3390/en14154649
fatcat:ailkfndy2bcatc27ycw45io2pm
Data-Stream-Based Intrusion Detection System for Advanced Metering Infrastructure in Smart Grid: A Feasibility Study
2015
IEEE Systems Journal
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We propose a realistic and reliable IDS architecture for the whole AMI system which consists of individual IDSs for three different levels of AMI's components: smart meter, data concentrator, and AMI headend ...
and then to a headend system in the utility side, the security of AMI is of great concern in the deployment of Smart Grid (SG). ...
new training instance is obtained from the data stream using stochastic gradient descent. ...
doi:10.1109/jsyst.2013.2294120
fatcat:7bowkudbg5dvznexkzvoduf24m
Intelligent energy management
2012
Proceedings of the Workshop on Performance Metrics for Intelligent Systems - PerMIS '12
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the weights are adapted using gradient descent as: And there is also: (5.5) Where denotes the derivative of with respect to θ l , which is adapted according to Equation 5.5. ...
which consists of a combination of the gradient descent algorithm and improved growing cell structure algorithm. ...
doi:10.1145/2393091.2393118
dblp:conf/permis/RaghavanK12
fatcat:6aldvjh2d5dhbksq47fp765l6q
A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management
2019
Energies
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based on the body of knowledge. ...
Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart ...
[144] ; methods discussed and evaluated on the basis of real-life data and the Confusion Matrix [145] ; Accuracy and Root Mean Square Error (RMSE) [9] ; energy cost savings [40] ; Precision, Specificity ...
doi:10.3390/en12244745
fatcat:eix222pupjcmddolute2qh4wia
An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings
2021
Energies
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First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. ...
The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. ...
Conflicts of Interest: The authors declare no conflict of interest regarding the publication of this paper. ...
doi:10.3390/en14113020
fatcat:a5bj4gvcvvb7vedq33giyzau3a
Short-Term Energy Consumption Forecasting at the Edge: A Federated Learning Approach
2021
IEEE Access
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By exploiting Federated Learning and Edge Computing capabilities, many Long Short-Term Memory (LSTM) models are locally trained by different users based on their own historical energy consumption samples ...
model, with implications on privacy and scalability. ...
We chose the Mean Absolute Error (MAE) as evaluation metric for the loss function, and we adopt the Adaptive Moment Estimation (Adam) variant of Stochastic Gradient Descent as optimization algorithm, with ...
doi:10.1109/access.2021.3094089
fatcat:sk4zz3oukjhbbk3lalykgoosl4
A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks
2020
IEEE Access
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The solution paradigm uses an Internet of Things (IoT) based on micro-services and containers. The design incorporates real-time streaming and infrastructure performance optimization to store data. ...
In this research, smart water meters were implemented, distributed, and installed in a regional area in Cairo while data were collected at uniform intervals then sent to the cloud instantly. ...
The Adam optimization algorithm is an extension to stochastic gradient descent that combines both the root mean square propagation, and the adaptive gradient algorithm [68] . ...
doi:10.1109/access.2020.3015655
fatcat:bcctlxmrljgd3gk6krjce4i4di
A Comprehensive Survey for Deep-Learning-Based Abnormality Detection in Smart Grids with Multimodal Image Data
2022
Applied Sciences
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Traditional approaches are also summarized together with their performance comparison with deep-learning-based approaches, based on which the necessity, seen in the surveyed literature, of adopting image-data-based ...
In this paper, we provide a comprehensive survey of the recent advances in abnormality detection in smart grids using multimodal image data, which include visible light, infrared, and optical satellite ...
YNKJXM20191246), which focuses on the construction of satellite remote-sensing technology for power applications and wide-area intelligent monitoring of environments. ...
doi:10.3390/app12115336
fatcat:ot7ptci7bzafng3ytwhkk53ole
IoT with Evolutionary Algorithm Based Deep Learning for Smart Irrigation System
2022
Computers Materials & Continua
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Secondly, these estimated outcomes are fed into the clustering technique to minimize the predicted error. ...
The goal of the IoTDL-SIS technique focuses on the design of smart irrigation techniques for effectual water utilization with less human interventions. ...
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study. ...
doi:10.32604/cmc.2022.021789
fatcat:klkb57pv6zfslbsmtemqwbwmo4
Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine
2019
Electronics
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To this aim, PHi-CiB relies on a pipeline of three algorithms: the Exponentially Weighted Moving Average, the Multivariate Adaptive Regression Spline, and the Linear Regression with Stochastic Gradient ...
error and an optimal horizontal scalability. ...
Adaptive Regression Spline, (iii) PP based on linear regression with stochastic gradient descent. ...
doi:10.3390/electronics8050491
fatcat:abyvmeoopvcnjiv2bjysbgpl2m
Survey of machine learning methods for detecting false data injection attacks in power systems
2020
IET Smart Grid
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SE is an essential process performed by the energy management system towards estimating unknown state variables based on system redundant measurements and network topology. ...
To overcome the limitations of traditional residual-based BDD approaches, datadriven solutions based on machine learning algorithms have been widely adopted for detecting malicious manipulation of sensor ...
MLPs use back-propagation training algorithms and the weights are updated using gradient descent to minimise the error function. Ashrafuzzaman et al. ...
doi:10.1049/iet-stg.2020.0015
fatcat:kzfjgzhmybgntijdpszkhk4p7i
HSIC Bottleneck based Distributed Deep Learning Model for Load Forecasting in Smart Grid with A Comprehensive Survey
2020
IEEE Access
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HSIC Bottleneck does not sustain the exploding gradients and vanishing gradients issues. It is based on a single layer-based distributed training process were used the Stochastic Gradient Descent (SGD ...
data and gradient-based learning methods. ...
CONFLICTS OF INTEREST The authors declare no conflict of interest. ...
doi:10.1109/access.2020.3040083
fatcat:tsqokovkm5gpfdsnm7bph73piu
Development of CNN-based visual recognition air conditioner for smart buildings
2020
Journal of Information Technology in Construction
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To address this challenge, this paper proposes a visual recognition method based on convolutional neural networks (CNN), which can intelligently interpret visual contents of surveillance cameras to identify ...
Demand-oriented HVAC control largely relies on accurate detection of building occupancy levels and locations. ...
Relative Errors versus Distance Based on the Data in Point
Face toward
the camera
Estimated
distance (meter)
Estimated
angle (degree)
Point
Face toward
the camera
Estimated
distance (meter ...
doi:10.36680/j.itcon.2020.021
fatcat:ds2rd6tg5zbjflnvgrjhr2ulzm
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