Machine learning in solar physics
The application of machine learning in solar physics has the potential to greatly enhance our
understanding of the complex processes that take place in the atmosphere of the Sun. By …
understanding of the complex processes that take place in the atmosphere of the Sun. By …
Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model
Accurate PV power forecasting is becoming a mandatory task to integrate the PV plant into
the electrical grid, scheduling and guaranteeing the safety of the power grid. In this paper, a …
the electrical grid, scheduling and guaranteeing the safety of the power grid. In this paper, a …
Prediction of solar cycle 25 using deep learning based long short-term memory forecasting technique
In the current work we have used the deep learning based long short-term memory model to
predict the strength and peak time of solar cycle 25 by employing the monthly smoothed …
predict the strength and peak time of solar cycle 25 by employing the monthly smoothed …
A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction
Solar activity has significant impacts on human activities and health. One most commonly
used measure of solar activity is the sunspot number. This paper compares three important …
used measure of solar activity is the sunspot number. This paper compares three important …
Predicting solar cycle 25 using an optimized long short-term memory model based on sunspot area data
H Zhu, H Chen, W Zhu, M He - Advances in Space Research, 2023 - Elsevier
In this paper, an optimized long short-term memory (LSTM) model was proposed to deal with
the monthly sunspot area (SSA) data, aiming to predict the peak amplitude of SSA and the …
the monthly sunspot area (SSA) data, aiming to predict the peak amplitude of SSA and the …
Prediction of amplitude and timing of solar cycle 25
We study the geomagnetic activity Ap-index in relation to sunspot number and area for the
interval covering Solar Cycles 17 to 24 (1932–2019), in view of the availability of data for the …
interval covering Solar Cycles 17 to 24 (1932–2019), in view of the availability of data for the …
Machine learning in solar physics
The application of machine learning in solar physics has the potential to greatly enhance our
understanding of the complex processes that take place in the atmosphere of the Sun. By …
understanding of the complex processes that take place in the atmosphere of the Sun. By …
Solar cycle 25 prediction using an optimized long short-term memory mode with F10. 7
H Zhu, W Zhu, M He - Solar Physics, 2022 - Springer
In this paper, an optimized long short-term memory (LSTM) model is proposed to deal with
the smoothed monthly F 10.7 data, aiming to predict the peak amplitude of F 10.7 and the …
the smoothed monthly F 10.7 data, aiming to predict the peak amplitude of F 10.7 and the …
Forecasting solar cycle 25 with physical model-validated recurrent neural networks
The Sun's activity, which is associated with the solar magnetic cycle, creates a dynamic
environment in space known as space weather. Severe space weather can disrupt space …
environment in space known as space weather. Severe space weather can disrupt space …
Prediction of nitrous oxide emission of a municipal wastewater treatment plant using LSTM-based deep learning models
X Xu, A Wei, S Tang, Q Liu, H Shi, W Sun - Environmental Science and …, 2024 - Springer
Accurate assessment of greenhouse gas emissions from wastewater treatment plants is
crucial for mitigating climate change. N2O is a potent greenhouse gas that is emitted from …
crucial for mitigating climate change. N2O is a potent greenhouse gas that is emitted from …