Artificial intelligence in physical sciences: Symbolic regression trends and perspectives
Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …
programming principles that integrates techniques and processes from heterogeneous …
[HTML][HTML] Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions
Although solar energy harnessing capacity varies considerably based on the employed
solar energy technology and the meteorological conditions, accurate direct normal …
solar energy technology and the meteorological conditions, accurate direct normal …
[HTML][HTML] Hybrid deep CNN-SVR algorithm for solar radiation prediction problems in Queensland, Australia
This study proposes a new hybrid deep learning (DL) model, the called CSVR, for Global
Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with …
Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with …
Read-First LSTM model: A new variant of long short term memory neural network for predicting solar radiation data
The prediction of solar radiation data is important for countries to reduce their dependence
on fossil fuels. Since the development of solar energy systems relies on an accurate …
on fossil fuels. Since the development of solar energy systems relies on an accurate …
Multi-step short-term solar radiation prediction based on empirical mode decomposition and gated recurrent unit optimized via an attention mechanism
X Kong, X Du, G Xue, Z Xu - Energy, 2023 - Elsevier
The utilization of sustainable and renewable energy for building heating is pivotal to
achieving sustainable development goals. However, the volatility of solar energy poses …
achieving sustainable development goals. However, the volatility of solar energy poses …
[HTML][HTML] Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings
Installing photovoltaic (PV) systems in buildings is one of the most effective strategies for
achieving sustainable energy goals and reducing carbon emissions. However, the …
achieving sustainable energy goals and reducing carbon emissions. However, the …
[HTML][HTML] Deep learning hybrid models with multivariate variational mode decomposition for estimating daily solar radiation
Solar energy is one of the renewable and clean energy sources. Accurate solar radiation
(SR) estimates are therefore needed in solar energy applications. Firstly, two deep learning …
(SR) estimates are therefore needed in solar energy applications. Firstly, two deep learning …
Transfer learning for renewable energy systems: A survey
Currently, numerous machine learning (ML) techniques are being applied in the field of
renewable energy (RE). These techniques may not perform well if they do not have enough …
renewable energy (RE). These techniques may not perform well if they do not have enough …
Hybrid power generation forecasting using CNN based BILSTM method for renewable energy systems
T Anu Shalini, B Sri Revathi - Automatika: časopis za automatiku …, 2023 - hrcak.srce.hr
Sažetak This paper presents the design of a grid-connected hybrid system using modified Z
source converter, bidirectional converter and battery storage system. The input sources for …
source converter, bidirectional converter and battery storage system. The input sources for …
Role of machine learning algorithms for wind power generation prediction in renewable energy management
The electrical energy demand is growing every day. Fossil fuel-based electrical power
generation pollutes the environment. So, to fulfil the electrical energy demand, clean …
generation pollutes the environment. So, to fulfil the electrical energy demand, clean …