A review on soft sensors for monitoring, control, and optimization of industrial processes
Over the past twenty years, numerous research outcomes have been published, related to
the design and implementation of soft sensors. In modern industrial processes, various types …
the design and implementation of soft sensors. In modern industrial processes, various types …
[HTML][HTML] Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology
Polymer electrolyte membrane (PEM) fuel cells are electrochemical devices that directly
convert the chemical energy stored in fuel into electrical energy with a practical conversion …
convert the chemical energy stored in fuel into electrical energy with a practical conversion …
A survey of machine learning models in renewable energy predictions
The use of renewable energy to reduce the effects of climate change and global warming
has become an increasing trend. In order to improve the prediction ability of renewable …
has become an increasing trend. In order to improve the prediction ability of renewable …
Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian …
M Sharifzadeh, A Sikinioti-Lock, N Shah - Renewable and Sustainable …, 2019 - Elsevier
Renewable energy from wind and solar resources can contribute significantly to the
decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless …
decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless …
Assessment of deep recurrent neural network-based strategies for short-term building energy predictions
Accurate and reliable building energy predictions can bring significant benefits for energy
conservations. With the development in smart buildings, massive amounts of building …
conservations. With the development in smart buildings, massive amounts of building …
Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning
Conversion of wet organic wastes into renewable energy is a promising way to substitute
fossil fuels and avoid environmental deterioration. Hydrothermal carbonization and pyrolysis …
fossil fuels and avoid environmental deterioration. Hydrothermal carbonization and pyrolysis …
[HTML][HTML] Advances of machine learning in multi-energy district communities‒mechanisms, applications and perspectives
Y Zhou - Energy and AI, 2022 - Elsevier
Energy paradigm transition towards the carbon neutrality requires combined and continuous
efforts in cleaner power production, advanced energy storages, flexible district energy …
efforts in cleaner power production, advanced energy storages, flexible district energy …
[HTML][HTML] A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights
The efforts to revolutionize electric power generation and produce clean and sustainable
electricity have led to the exploration of renewable energy systems (RES). This form of …
electricity have led to the exploration of renewable energy systems (RES). This form of …
Dynamic NOX emission concentration prediction based on the combined feature selection algorithm and deep neural network
Z Tang, S Wang, Y Li - Energy, 2024 - Elsevier
The development of an accurate nitrogen oxide (NO x) prediction model is difficult because
of multiple parameters, strong coupling, and long delay time of selective catalytic reduction …
of multiple parameters, strong coupling, and long delay time of selective catalytic reduction …
Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN …
Optimal utilisation of the sun's freely available energy to generate electricity requires efficient
predictive models of global solar radiation (GSR). These are necessary to provide solar …
predictive models of global solar radiation (GSR). These are necessary to provide solar …