Predicting Solar cycle 25 using an optimized long short-term memory model based on sunspot area data

Zhu, Hongbing; Chen, Haoze; Zhu, Wenwei; He, Mu

doi:10.1016/j.asr.2023.01.042

Cited by 13 publications

(4 citation statements)

References 36 publications

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“…The prediction results of the amplitude of SC 25 using the flattening index in April 2023 suggest that SC 25 will be significantly different from SC 24. This aligns with several other predictions that have used spectral methods (Han and Yin, 2019) and long short-term memory-based models (Zhu et al, 2023). This prediction differs from many solar cycle predictions that are based on polar-field precursors, which suggest that SC 25 will be similar to or only slightly higher than SC 24 (Nandy, 2021).…”

Section: Discussionsupporting

confidence: 84%

Coronal Flattening Index of Total Solar Eclipse on 20 April 2023 and the Prediction of Solar Cycle 25 Maximum

Pangestu,

Yusuf,

Prastyo

et al. 2023

Preprint

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We observed a total solar eclipse (TSE) on 20 April 2023 in Timor Leste and analyzed the isophote of the coronal image. We obtained a coronal flattening index (ε) with a value of 0.10 and a phase value (Φ) of 0.70, indicating that the TSE had a pre-maximum coronal type. Based on the composite image of the corona, four large streamers were observed in the northern and southern hemispheres of the Sun, with small streamers scattered uniformly in the solar disk. It indicates that the Sun was in the ascending phase of its activity cycle. Moreover, we developed two models to predict the maximum of the current solar cycle (Wmax) using the Smoothed Sunspot Number (SSN) and ε from all TSE data during the ascending phase (positive Φ) from 1893 to 2013. We used Multi Linear Correlation (MLC) and 2nd-order Polynomial Regression (PR2) to predict the value of Wmax for the solar cycle 25 (SC 25) based on the observations of TSE on 20 April 2023. We obtained values of 175.5 ± 27.9 and 168.2 ± 25.8 using MLC and PR2, respectively, with the estimated peak time in September 2024. These results indicate that SC 25 will be stronger than the solar cycle 24 (SC 24).

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Section: Discussionsupporting

confidence: 84%

Coronal Flattening Index of Total Solar Eclipse on 20 April 2023 and the Prediction of Solar Cycle 25 Maximum

Pangestu,

Yusuf,

Prastyo

et al. 2023

Preprint

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“…Due to the long history of sunspot records, there is a very good knowledge about the Sun's activity level changing with a period of about 11 years. Against predictions from previous studies, recent investigations expect the maximum to be higher compared to cycle 24 and to occur already mid 2024 to early 2025 (McIntosh et al, 2023;Zhu et al, 2023;Nagovitsyn and Ivanov, 2023).…”

Section: Introductionmentioning

confidence: 63%

SODA - a tool to predict storm induced orbit decays for low Earth orbiting satellites

Krauss,

Drescher,

Temmer

et al. 2024

J. Space Weather Space Clim.

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Due to the rapidly increasing technological progress in the last decades, the issue of space weather and its influences on our everyday life gains more and more importance. Today, satellite-based navigation plays a key role in aviation, logistics and transportation systems. With the strong rise of the current solar cycle 25 the number and intensity of solar eruptions increased. The forecasting tool SODA (Satellite Orbit DecAy) is based on an interdisciplinary analysis of space geodetic observations and solar wind in situ measurements. It predicts the effect of coronal mass ejections from their in-situ measured counterparts (ICME) on the altitude of low Earth orbiting satellites at 490 km with a lead time of about 20 hours. Additionally, it classifies the severeness of the expected geomagnetic storm in the form of the Space Weather G–scale from the National Oceanic and Atmospheric Administration (NOAA). For the establishment and validation of SODA, we examined 360 ICME events over a period of 21 years. Appropriated variations in the thermospheric neutral mass density, were derived mainly from measurements of the Gravity Recovery and Climate Experiment (GRACE) satellite mission. Related changes in the interplanetary magnetic field component Bz were investigated from real-time measurements using data from spacecraft located at the Lagrange point L1. The analysis of the ICME induced orbit decays and the interplanetary magnetic field showed a strong correlation as well as a time delay between the ICME and the associated thermospheric response. The derived results are implemented in the real- forecasting tool SODA, which integrated in the Space Safety Program Ionospheric Weather Expert Service Center; I.161 of the Space Agency (ESA).

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“…In another study, Ojo [14] considered air temperature, solar radiation, and wind speed data from the National Aeronautics and Space Administration and evaluated ten existing models. Besides, Zhu et al [15], evaluated the correlations between solar irradiance intensity, atmospheric density, cloudiness, wind speed, relative humidity, and ambient temperature using the Pearson correlation coefficient. However, in the context of the Borneo region, Malaysia, no major study is found in the literature to examine meteorological parameters in depth to find the correlation with solar power generation.…”

Section: Figure 1 Solar Irradiance Profile In Malaysiamentioning

confidence: 99%

Analyzing meteorological parameters using Pearson correlation coefficient and implementing machine learning models for solar energy prediction in Kuching, Sarawak

Tan,

N. Afrouzi,

Ahmed

et al. 2024

fusus

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Solar energy is one of the clean renewable energy sources that can offset the rising consumption of fossil fuels. However, the meteorological parameters, such as solar irradiance, ambient and solar module temperatures, relative humidity, etc., constantly change, and so does the solar power generation. Such variations cause instability in the power grid operation due to injecting an unpredicted amount of power. Hence, solar energy prediction models capable of learning from past weather data and predicting future energy generation are highly desired for grid operation and planning. The objective of this study is to determine the suitable meteorological parameters for the solar energy prediction model based on the Pearson correlation coefficient and to implement them in different machine learning models. It is found in this study that five meteorological parameters, namely Air temperature, cloud opacity, global tilted irradiance, relative humidity, and zenith angle, correlate highly with solar energy generation. Later, based on the correlations, four machine-learning models were implemented to predict the solar power for Kuching, Sarawak. The accuracy of the models is measured through standard matrices such as root mean square error, mean square error, mean absolute error, and R-squared value.

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Predicting Solar cycle 25 using an optimized long short-term memory model based on sunspot area data

Cited by 13 publications

References 36 publications

Coronal Flattening Index of Total Solar Eclipse on 20 April 2023 and the Prediction of Solar Cycle 25 Maximum

Coronal Flattening Index of Total Solar Eclipse on 20 April 2023 and the Prediction of Solar Cycle 25 Maximum

SODA - a tool to predict storm induced orbit decays for low Earth orbiting satellites

Analyzing meteorological parameters using Pearson correlation coefficient and implementing machine learning models for solar energy prediction in Kuching, Sarawak

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