Wavelet-Gaussian process regression model for forecasting daily solar radiation in the Saharan climate

Ferkous, Khaled; Chellali, Farouk; Kouzou, Abdalah; Bekkar, Belgacem

doi:10.1093/ce/zkab012

Cited by 10 publications

(5 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Wavelet packet transform can decompose both low-frequency and high-frequency components of the same scale, and the decomposition has neither redundancy nor omission, a better time-frequency localization analysis can be conducted on the signals containing a large amount of medium and high frequency information [17]. Wavelet packet analysis performs adaptive frequency band decomposition according to the inherent characteristics of GPR signal, thus greatly improving the time-frequency resolution, so wavelet packet analysis has a broader application prospect [18].…”

Section: Wavelet Packet Theorymentioning

confidence: 99%

Geological detection of hard rocks by GPR and signal time-frequency characteristics analysis in urban underground trenchless construction

Zhang,

Deng

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The hard rocks in the stratum can pose safety risks and hinder the progress of urban underground tunnel construction using shield and jacking methods, thereby reducing construction efficiency and increasing construction costs. This paper utilizes wavelet scale energy spectrum, wavelet packet theory and statistical methods to conduct research on the detection of special geological formations such as hard rocks and voids, as well as the analysis of their signal time-frequency characteristics based on the ground-penetrating radar (GPR) technique. On the basis of calibrating the permittivity of different types of rock blocks, we established a forward model for detecting hard rocks and voids, and the simulated signals were analyzed in the time and frequency domains. Subsequently, laboratory experiments were conducted to perform GPR tests on different types of hard rocks in natural and water-saturated states and voids, to explore the time-frequency characteristics, frequency band energy variations, and statistical patterns of typical single-trace signals. The results show that the granite detection signal contains more low-frequency components, the sandstone detection signal contains more medium-low frequency components, while the limestone detection signal contains more medium-high frequency components in their natural state; the signal from the karst cave has relatively more low-frequency components than the signal from the empty cavity. The geometric shape of the rock has no influence on the dominant frequency and time-frequency distribution of its reflection signal. Generally, rocks with higher rebound values (hardness) also exhibit larger variance and standard deviation in frequency band energy. The research has important theoretical significance and practical value for the measurement and assessment of special geological features such as hard rocks and voids in urban underground trenchless construction.

show abstract

Section: Wavelet Packet Theorymentioning

confidence: 99%

Geological detection of hard rocks by GPR and signal time-frequency characteristics analysis in urban underground trenchless construction

Zhang,

Deng

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Furthermore, recent machine learning approaches such as Extreme Gradient Boosting (XGBoost) and Gaussian Process Regression (GPR) have shown exceptional promise in the prediction of renewable energy [315]- [318]. Li et al [309] proved the high accuracy of XGBoost in forecasting solar radiation using public data, while Ferkous et al [310] employed a hybrid technique of Wavelet-GPR to obtain excellent prediction performance with R 2 of 0.923 and RMSE of 2.4191. The research conducted by Das et al [175] demonstrates that other machine learning techniques, such as regression based on Support Vector Machines (SVM), have also been used to predict solar power production.…”

Section: Svm Based Regression Solar Power Generation Prediction Modelmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

show abstract

“…Gaussian Process Regression (GPR) provides a nonparametric, Bayesian method of regression with its probabilistic framework for prediction and uncertainty quantification Marrel and Iooss, 2024). GPR is used to estimate and forecast the fundamentally variable and environmentdependent outputs of renewable energy sources such as solar panels and wind turbines (Ferkous et al, 2021;Lio et al, 2021). Since GPR can produce confidence intervals in addition to point estimates, it truly excels in assessing the accuracy of energy estimates (Baiz et al, 2020;Huang et al, 2019).…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Le,

Paramasivam,

Adril

et al. 2024

Int. J. Renew. Energy Dev.

View full text Add to dashboard Cite

This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects

show abstract

Wavelet-Gaussian process regression model for forecasting daily solar radiation in the Saharan climate

Cited by 10 publications

References 39 publications

Geological detection of hard rocks by GPR and signal time-frequency characteristics analysis in urban underground trenchless construction

Geological detection of hard rocks by GPR and signal time-frequency characteristics analysis in urban underground trenchless construction

Artificial intelligence applications in solar energy

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Contact Info

Product

Resources

About