AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
In theory, building automation and management systems (BAMSs) can provide all the
components and functionalities required for analyzing and operating buildings. However, in …
components and functionalities required for analyzing and operating buildings. However, in …
Applications of artificial intelligence for energy efficiency throughout the building lifecycle: An overview
Abstract The use of Artificial Intelligence (AI) technologies in buildings can assist in reducing
energy consumption through enhanced control, automation, and reliability. This review aims …
energy consumption through enhanced control, automation, and reliability. This review aims …
Long short-term memory network-based metaheuristic for effective electric energy consumption prediction
The Electric Energy Consumption Prediction (EECP) is a complex and important process in
an intelligent energy management system and its importance has been increasing rapidly …
an intelligent energy management system and its importance has been increasing rapidly …
[HTML][HTML] Deep neural network with empirical mode decomposition and Bayesian optimisation for residential load forecasting
In the context of a resilient energy system, accurate residential load forecasting has become
a non-trivial requirement for ensuring effective management and planning strategy/policy …
a non-trivial requirement for ensuring effective management and planning strategy/policy …
Emotion recognition based on brain-like multimodal hierarchical perception
X Zhu, Y Huang, X Wang, R Wang - Multimedia Tools and Applications, 2024 - Springer
Emotion recognition has gained prominence in diverse applications ranging from safe
driving and e-commerce to healthcare. Traditional approaches have often relied on single …
driving and e-commerce to healthcare. Traditional approaches have often relied on single …
[HTML][HTML] Age and gender recognition using a convolutional neural network with a specially designed multi-attention module through speech spectrograms
Speech signals are being used as a primary input source in human–computer interaction
(HCI) to develop several applications, such as automatic speech recognition (ASR), speech …
(HCI) to develop several applications, such as automatic speech recognition (ASR), speech …
A CNN-Assisted deep echo state network using multiple Time-Scale dynamic learning reservoirs for generating Short-Term solar energy forecasting
M Ishaq, S Kwon - Sustainable energy technologies and assessments, 2022 - Elsevier
The integration of renewable energy generation presented an important development
around the globe and conveys countless financial, commercial, and environmental …
around the globe and conveys countless financial, commercial, and environmental …
A comparative study of AI methods on renewable energy prediction for smart grids: case of Turkey
Fossil fuels still have emerged as the predominant energy source for power generation on a
global scale. In recent years, Turkey has experienced a notable decrease in the production …
global scale. In recent years, Turkey has experienced a notable decrease in the production …
Virtual power plant optimization in smart grids: A narrative review
Virtual power plants (VPPs) are promising solutions to address the decarbonization and
energy efficiency goals in the smart energy grid. They assume the coordination of local …
energy efficiency goals in the smart energy grid. They assume the coordination of local …
[HTML][HTML] AB-net: A novel deep learning assisted framework for renewable energy generation forecasting
Renewable energy (RE) power plants are deployed globally because the renewable energy
sources (RESs) are sustainable, clean, and environmentally friendly. However, the demand …
sources (RESs) are sustainable, clean, and environmentally friendly. However, the demand …