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 …
Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model
Solar radiation is one of the cleanest sources of renewable energy, and it affects the carbon
sink functions of terrestrial ecosystems. Although efforts have been made to establish solar …
sink functions of terrestrial ecosystems. Although efforts have been made to establish solar …
Towards intelligent building energy management: AI-based framework for power consumption and generation forecasting
Due to global warming and climate changes, buildings including residential and commercial
are significant contributors to energy consumption. To this end, net zero energy building …
are significant contributors to energy consumption. To this end, net zero energy building …
Deep learning in energy modeling: Application in smart buildings with distributed energy generation
Buildings are responsible for 33% of final energy consumption, and 40% of direct and
indirect CO 2 emissions globally. While energy consumption is steadily rising globally …
indirect CO 2 emissions globally. While energy consumption is steadily rising globally …
Accurately forecasting solar radiation distribution at both spatial and temporal dimensions simultaneously with fully-convolutional deep neural network model
Accurately forecasting solar radiation is of great significance to solar energy utilization. To
forecast the spatial and temporal distributions of solar radiation simultaneously, a deep …
forecast the spatial and temporal distributions of solar radiation simultaneously, a deep …
A review of the applications of artificial intelligence in renewable energy systems: An approach-based study
Recent advancements in data science and artificial intelligence, as well as the development
of clean and sustainable energy sources, have created numerous opportunities for energy …
of clean and sustainable energy sources, have created numerous opportunities for energy …
[HTML][HTML] Random Forest model to predict solar water heating system performance
I Lillo-Bravo, J Vera-Medina, C Fernandez-Peruchena… - Renewable Energy, 2023 - Elsevier
This research proposes a Random Forest RF model to replace the experimental tests
required by the ISO 9459–5: 2007 for predicting the annual energy supplied and the solar …
required by the ISO 9459–5: 2007 for predicting the annual energy supplied and the solar …
Forecasting solar-thermal systems performance under transient operation using a data-driven machine learning approach based on the deep operator network …
Modeling and prediction of the dynamic behavior of thermal systems operating under
intermittent energy input and variable load requirements represent one of the greatest …
intermittent energy input and variable load requirements represent one of the greatest …
Short-term energy use prediction of solar-assisted water heating system: Application case of combined attention-based LSTM and time-series decomposition
A Heidari, D Khovalyg - Solar Energy, 2020 - Elsevier
With improved insulation of building envelopes and the use of low-temperature space
heating systems, the share of energy use for domestic hot water (DHW) production in …
heating systems, the share of energy use for domestic hot water (DHW) production in …
[HTML][HTML] A review from design to control of solar systems for supplying heat in industrial process applications
The use of solar thermal systems to produce heat for industrial processes is a feasible option
that is gaining increasing interest in recent years as an initiative toward the zero-carbon …
that is gaining increasing interest in recent years as an initiative toward the zero-carbon …