Results 81 to 90 of about 27,643 (149)

A Deep Learning‐to‐learning Based Control system for renewable microgrids

IET Renewable Power Generation, EarlyView., 2023
This paper focuses on wind power forecasting and optimal management and control of renewable microgrids (MGs) in a realistic environment. For robust and efficient scheduling of MG, an optimal framework based on TLBO and deep learning is proposed. Abstract In terms of microgrids (MGs) operation, optimal control and management are vital issues that must ...
Hossein Mohammadi, Shiva Jokar, Mojtaba Mohammadi, Abdollah Kavousi Fard, Morteza Dabbaghjamanesh, Mazaher Karimi   +5 more
wiley   +1 more source

Evaluating Multi‐Label Machine Learning Models for Smart Home Environments

Software: Practice and Experience, EarlyView.
ABSTRACT Context Smart home devices have become increasingly popular in modern households, powered by the Internet of Things (IoT) advances. The data generated by smart devices can provide valuable insights into users' behavior and preferences. By analyzing the data, one can understand how people interact with their homes, thus creating a “smart home ...
Diego Corrêa da Silva, Denis Robson Dantas Boaventura, Mayki dos Santos Oliveira, Jander Pereira Santos Junior, Eduardo Ferreira da Silva, Eduardo Santana de Almeida, Cássio V. S. Prazeres, Ivan do Carmo Machado, Maycon Leone Maciel Peixoto, Gustavo Bittencourt Figueiredo, Frederico Araújo Durão   +10 more
wiley   +1 more source

Mitigating anomalous electricity consumption in smart cities using an AI‐based stacked‐generalization technique

IET Renewable Power Generation, EarlyView., 2023
In this paper, a novel machine learning based stacked generalization method is proposed for dealing with the cyber‐physical theft issue in the smart grids and also to deal with the anomalous electricity consumption by the users. Abstract Energy management and efficient asset utilization play an important role in the economic development of a country ...
Arshid Ali, Laiq Khan, Nadeem Javaid, Safdar Hussain Bouk, Abdulaziz Aldegheishem, Nabil Alrajeh   +5 more
wiley   +1 more source

Advanced Hybrid Techniques for Cyberattack Detection and Defense in IoT Networks

SECURITY AND PRIVACY, Volume 8, Issue 2, March/April 2025.
ABSTRACT The Internet of Things (IoT) represents a vast network of devices connected to the Internet, making it easier for users to connect to modern technology. However, the complexity of these networks and the large volume of data pose significant challenges in protecting them from persistent cyberattacks, such as distributed denial‐of‐service (DDoS)
Zaed S. Mahdi, Rana M. Zaki, Laith Alzubaidi   +2 more
wiley   +1 more source

Data‐driven energy sharing for multi‐microgrids with building prosumers: A hybrid learning approach

IET Renewable Power Generation, EarlyView., 2023
A hybrid learning approach is proposed in this study. XGBoost‐based demand response model of prosumers is first trained by supervised learning and then embedded into the MADRL environment. And an XGBoost‐embedded MADDPG algorithm is adopted to obtain the optimal scheduling strategy of MMGs with building prosumers through off‐line centralized training ...
Haonan Sun, Nian Liu, Lu Tan, Jianpei Han   +3 more
wiley   +1 more source

Adaptive Neuro‐Fuzzy Inference System‐Long Short‐Term Memory Hybrid Model to Forecast Castability of Al‐Killed Steel Prior to Continuous Casting

steel research international, EarlyView.
A novel, hybrid, data‐driven model is developed to assist steelmakers in minimizing production interruptions caused by submerged entry nozzle (SEN) clogging. The modeling architecture is specifically designed to analyze variations in steel chemistry and stopper rod changes, employing a data‐driven approach combined with metallurgical and industrial ...
Sudhanshu Kuthe, Izaskun Alonso Oña, Björn Glaser   +2 more
wiley   +1 more source

Evaluation of machine learning and deep learning algorithms for fire prediction in Southeast Asia. [PDF]

Sci Rep
Eaturu A, Vadrevu KP.
europepmc   +1 more source

Physics‐Informed Data‐Driven Prediction of Submerged Entry Nozzle Clogging with the Aid of Ab Initio Repository

steel research international, EarlyView.
A physics‐informed data‐driven model is developed to assist steelmakers in minimizing production interruptions caused by submerged entry nozzle (SEN) clogging. To further enhance accuracy, methodology involving use of ab‐initio repository is developed. “Clogging Factor” parameter is proposed to monitor the output generated .
Sudhanshu Kuthe, Clas Persson, Björn Glaser   +2 more
wiley   +1 more source

A roll attitude determination method based on the jamming energy of GEO satellites and an LSTM neural network. [PDF]

Sci Rep
Li R, Feng L, Wu P, Deng X, Shi H.
europepmc   +1 more source

Trust at Your Own Peril: A Mixed Methods Exploration of the Ability of Large Language Models to Generate Expert‐Like Systems Engineering Artifacts and a Characterization of Failure Modes

Systems Engineering, EarlyView.
ABSTRACT Multi‐purpose large language models (LLMs), a subset of generative artificial intelligence (AI), have recently made significant progress. While expectations for LLMs to assist systems engineering (SE) tasks are paramount; the interdisciplinary and complex nature of systems, along with the need to synthesize deep‐domain knowledge and ...
Taylan G. Topcu, Mohammed Husain, Max Ofsa, Paul Wach   +3 more
wiley   +1 more source