Adventures in data analysis: A systematic review of Deep Learning techniques for pattern recognition in cyber-physical-social systems
Abstract Machine Learning (ML) and Deep Learning (DL) have achieved high success in
many textual, auditory, medical imaging, and visual recognition patterns. Concerning the …
many textual, auditory, medical imaging, and visual recognition patterns. Concerning the …
Thermoelectric energy harvesting for internet of things devices using machine learning: A review
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 …
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 …
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
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 …
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
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 …
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 …
This study employs soft computing techniques, including artificial neural network (ANN)
models and gene expression programming (GEP), to enhance the prediction of ultimate load …
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
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 …
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
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 …
(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) …
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
Active insulation systems (AIS) have been conceptualized to make buildings more
responsive to changing environmental conditions, creating energy efficiency and flexibility …
responsive to changing environmental conditions, creating energy efficiency and flexibility …