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 …
Artificial neural networks for photovoltaic power forecasting: a review of five promising models
Solar energy is largely dependent on weather conditions, resulting in unpredictable,
fluctuating, and unstable photovoltaic (PV) power outputs. Thus, accurate PV power …
fluctuating, and unstable photovoltaic (PV) power outputs. Thus, accurate PV power …
Microgrid digital twins: Concepts, applications, and future trends
Following the fourth industrial revolution, and with the recent advances in information and
communication technologies, the digital twinning concept is attracting the attention of both …
communication technologies, the digital twinning concept is attracting the attention of both …
LOWESS smoothing and Random Forest based GRU model: A short-term photovoltaic power generation forecasting method
Y Dai, Y Wang, M Leng, X Yang, Q Zhou - Energy, 2022 - Elsevier
Accurate prediction of photovoltaic power generation is vital to guarantee smooth operation
of power stations and ensure users' electricity consumption. As a good forecasting tool …
of power stations and ensure users' electricity consumption. As a good forecasting tool …
[HTML][HTML] Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control
In recent years, the photovoltaic generation installed capacity has been steadily growing
thanks to its inexhaustible and non-polluting characteristics. However, solar generators are …
thanks to its inexhaustible and non-polluting characteristics. However, solar generators are …
Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output
Solar radiation's intermittent and fluctuating nature poses severe limitations for most
applications. Accurate prediction of solar radiation is an essential factor in predicting the …
applications. Accurate prediction of solar radiation is an essential factor in predicting the …
Hybrid convolutional neural network-multilayer perceptron model for solar radiation prediction
Urgent transition from the dependence on fossil fuels towards renewable energies requires
more solar photovoltaic power to be connected to the electricity grids, with reliable supply …
more solar photovoltaic power to be connected to the electricity grids, with reliable supply …
[HTML][HTML] Forecasting intra-hour solar photovoltaic energy by assembling wavelet based time-frequency analysis with deep learning neural networks
Due to the expected lack of fossil fuels in near future as well as climate change produced by
greenhouse effect as consequence of environmental emissions, renewable energy …
greenhouse effect as consequence of environmental emissions, renewable energy …
Prediction of diffuse solar radiation by integrating radiative transfer model and machine-learning techniques
Diffuse radiation is a major component of solar radiation that is important in carbon
exchanges and material, energy, and information flows in agricultural ecosystems; however …
exchanges and material, energy, and information flows in agricultural ecosystems; however …
Exploring the convergence of Metaverse, Blockchain, Artificial Intelligence, and digital twin for pioneering the digitization in the envision smart grid 3.0
The ongoing evolution of the Metaverse, digital twin (DT), artificial intelligence (AI), and
Blockchain technologies is fundamentally transforming the utilization of sustainable energy …
Blockchain technologies is fundamentally transforming the utilization of sustainable energy …