Adventures in data analysis: A systematic review of Deep Learning techniques for pattern recognition in cyber-physical-social systems

Z Amiri, A Heidari, NJ Navimipour, M Unal… - Multimedia Tools and …, 2024 - Springer
Abstract Machine Learning (ML) and Deep Learning (DL) have achieved high success in
many textual, auditory, medical imaging, and visual recognition patterns. Concerning the …

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

T Kucova, M Prauzek, J Konecny… - CAAI Transactions …, 2023 - Wiley Online Library
Initiatives to minimise battery use, address sustainability, and reduce regular maintenance
have driven the challenge to use alternative power sources to supply energy to devices …

Deep learning hyperparameter optimization: Application to electricity and heat demand prediction for buildings

A Morteza, AA Yahyaeian, M Mirzaeibonehkhater… - Energy and …, 2023 - Elsevier
Optimal planning and operation studies of modern energy systems are tied up with medium
to long-term predictions of energy demand. Deep learning algorithms have recently become …

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

P Movahed, S Taheri, A Razban - Applied Energy, 2023 - Elsevier
Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will
eventually lead to a range of HVAC system failures, resulting in excessive energy …

[HTML][HTML] Design optimization of ocean renewable energy converter using a combined Bi-level metaheuristic approach

E Amini, M Nasiri, NS Pargoo, Z Mozhgani… - Energy Conversion and …, 2023 - Elsevier
In recent years, there has been an increasing interest in renewable energies in view of the
fact that fossil fuels are the leading cause of catastrophic environmental consequences …

[HTML][HTML] Prediction of pull-out behavior of timber glued-in glass fiber reinforced polymer and steel rods under various environmental conditions based on ANN and …

MM Taleshi, N Tajik, A Mahmoudian… - Case Studies in …, 2024 - Elsevier
This study employs soft computing techniques, including artificial neural network (ANN)
models and gene expression programming (GEP), to enhance the prediction of ultimate load …

[HTML][HTML] Enhancing the performance of hybrid wave-wind energy systems through a fast and adaptive chaotic multi-objective swarm optimisation method

M Neshat, NY Sergiienko, MM Nezhad, LSP da Silva… - Applied Energy, 2024 - Elsevier
Hybrid offshore renewable energy platforms have been proposed to optimise power
production and reduce the levelised cost of energy by integrating or co-locating several …

[HTML][HTML] Prediction of composite mechanical properties: Integration of deep neural network methods and finite element analysis

K Gholami, F Ege, R Barzegar - Journal of Composites Science, 2023 - mdpi.com
Extracting the mechanical properties of a composite hydrogel; eg, bioglass (BG)–collagen
(COL), is often difficult due to the complexity of the experimental procedure. BGs could be …

Power system flexibility analysis using net-load forecasting based on deep learning considering distributed energy sources and electric vehicles

ET Rizi, M Rastegar, A Forootani - Computers and Electrical Engineering, 2024 - Elsevier
Today, wind and solar energy sources have opened their place in the power system due to
their environmental appeal. With the presence of these renewable energy sources (RESs) …

Tradeoffs in optimization of Active Insulation Systems and HVAC: A case study in residential buildings

A Sepehri, G Pavlak - Science and Technology for the Built …, 2023 - Taylor & Francis
Active insulation systems (AIS) have been conceptualized to make buildings more
responsive to changing environmental conditions, creating energy efficiency and flexibility …