A survey of machine learning models in renewable energy predictions

JP Lai, YM Chang, CH Chen, PF Pai - Applied Sciences, 2020 - mdpi.com
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 …

Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model

Y Lu, L Wang, C Zhu, L Zou, M Zhang, L Feng… - … and Sustainable Energy …, 2023 - Elsevier
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 …

Towards intelligent building energy management: AI-based framework for power consumption and generation forecasting

SU Khan, N Khan, FUM Ullah, MJ Kim, MY Lee… - Energy and …, 2023 - Elsevier
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 …

Deep learning in energy modeling: Application in smart buildings with distributed energy generation

SA Nabavi, NH Motlagh, MA Zaidan, A Aslani… - IEEE …, 2021 - ieeexplore.ieee.org
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 …

Accurately forecasting solar radiation distribution at both spatial and temporal dimensions simultaneously with fully-convolutional deep neural network model

Z Ruan, W Sun, Y Yuan, H Tan - Renewable and Sustainable Energy …, 2023 - Elsevier
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 …

A review of the applications of artificial intelligence in renewable energy systems: An approach-based study

M Shoaei, Y Noorollahi, A Hajinezhad… - Energy Conversion and …, 2024 - Elsevier
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 …

[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 …

Forecasting solar-thermal systems performance under transient operation using a data-driven machine learning approach based on the deep operator network …

JD Osorio, Z Wang, G Karniadakis, S Cai… - Energy Conversion and …, 2022 - Elsevier
Modeling and prediction of the dynamic behavior of thermal systems operating under
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 …

[HTML][HTML] A review from design to control of solar systems for supplying heat in industrial process applications

JD Gil, A Topa, JD Álvarez, JL Torres… - … and Sustainable Energy …, 2022 - Elsevier
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 …