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Artificial intelligence applications for microgrids integration and management of hybrid renewable energy sources
The integration of renewable energy sources (RESs) has become more attractive to provide
electricity to rural and remote areas, which increases the reliability and sustainability of the …
electricity to rural and remote areas, which increases the reliability and sustainability of the …
[HTML][HTML] Applications of reinforcement learning in energy systems
Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …
renewable energy technologies and improve efficiencies, leading to the integration of many …
[HTML][HTML] Towards energy freedom: Exploring sustainable solutions for energy independence and self-sufficiency using integrated renewable energy-driven hydrogen …
In the pursuit of sustainable energy solutions, the integration of renewable energy sources
and hydrogen technologies has emerged as a promising avenue. This paper introduces the …
and hydrogen technologies has emerged as a promising avenue. This paper introduces the …
[HTML][HTML] Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach
An accurate solar energy forecast is of utmost importance to allow a higher level of
integration of renewable energy into the controls of the existing electricity grid. With the …
integration of renewable energy into the controls of the existing electricity grid. With the …
Predicting uniaxial compressive strength of rocks using ANN models: incorporating porosity, compressional wave velocity, and schmidt hammer data
The unconfined compressive strength (UCS) of intact rocks is crucial for engineering
applications, but traditional laboratory testing is often impractical, especially for historic …
applications, but traditional laboratory testing is often impractical, especially for historic …
State of the art of machine learning models in energy systems, a systematic review
Machine learning (ML) models have been widely used in the modeling, design and
prediction in energy systems. During the past two decades, there has been a dramatic …
prediction in energy systems. During the past two decades, there has been a dramatic …
A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation
The energy consumption of major equipment in residential and industrial facilities can be
minimized through a variety of cost-effective energy-saving measures. Most saving …
minimized through a variety of cost-effective energy-saving measures. Most saving …
[HTML][HTML] A review of the use of artificial intelligence methods in infrastructure systems
The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the
growth of digitalisation and has the potential to enable the 'system of systems' approach …
growth of digitalisation and has the potential to enable the 'system of systems' approach …
A review on time series forecasting techniques for building energy consumption
Energy consumption forecasting for buildings has immense value in energy efficiency and
sustainability research. Accurate energy forecasting models have numerous implications in …
sustainability research. Accurate energy forecasting models have numerous implications in …
Machine learning for estimation of building energy consumption and performance: a review
Ever growing population and progressive municipal business demands for constructing new
buildings are known as the foremost contributor to greenhouse gasses. Therefore …
buildings are known as the foremost contributor to greenhouse gasses. Therefore …