Validation of the Accuracy of NASA Solar Irradiation Data for Four African Regions

Jed, Mohamed; Ihaddadène, Nabila; Jed, Mohamed El Hacen; Ihaddadène, Razika; Bah, Menny El

doi:10.18280/ijsdp.170103

Cited by 10 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The National Aeronautics and Space Administration (NASA) has been providing data at 0.5 • spatial resolution worldwide since 1981 to support the renewable energy and agriculture sectors, and recently launched the Projection of Worldwide Energy Resources (POWER) project to produce long-term climate variables [51]. The user-friendly data access interface is the main focus of NASA POWER, and every dataset is available at four temporal levels: hourly, daily, monthly, and climatologically [52] Numerous studies are conducted to validate the performance of NASA POWER data in various locations around the world; i.e., Jordan [53], Iran [54], Africa [55], Brazil [56], Iraq [57], and Malaysia [58]. In northern Peninsular Malaysia, Bandira et al (2022) reported that NASA POWER performed statistically satisfactorily for radiation and maximum and minimum temperatures, but less successfully for precipitation, wind speed, and mixing ratio [59].…”

Section: Data Collectionmentioning

confidence: 99%

Enhancing Solar Radiation Forecasting in Diverse Moroccan Climate Zones: A Comparative Study of Machine Learning Models with Sugeno Integral Aggregation

Mendyl,

Demir,

Omar

et al. 2024

Atmosphere

View full text Add to dashboard Cite

Hourly solar radiation (SR) forecasting is a vital stage in the efficient deployment of solar energy management systems. Single and hybrid machine learning (ML) models have been predominantly applied for precise hourly SR predictions based on the pattern recognition of historical heterogeneous weather data. However, the integration of ML models has not been fully investigated in terms of overcoming irregularities in weather data that may degrade the forecasting accuracy. This study investigated a strategy that highlights interactions that may exist between aggregated prediction values. In the first investigation stage, a comparative analysis was conducted utilizing three different ML models including support vector machine (SVM) regression, long short-term memory (LSTM), and multilayer artificial neural networks (MLANN) to provide insights into their relative strengths and weaknesses for SR forecasting. The comparison showed the proposed LSTM model had the greatest contribution to the overall prediction of six different SR profiles from numerous sites in Morocco. To validate the stability of the proposed LSTM, Taylor diagrams, violin plots, and Kruskal–Wallis (KW) tests were also utilized to determine the robustness of the model’s performance. Secondly, the analysis found coupling the models outputs with aggregation techniques can significantly improve the forecasting accuracy. Accordingly, a novel aggerated model that integrates the forecasting outputs of LSTM, SVM, MLANN with Sugeno λ-measure and Sugeno integral named (SLSM) was proposed. The proposed SLSM provides spatially and temporary interactions of information that are characterized by uncertainty, emphasizing the importance of the aggregation function in mitigating irregularities associated with SR data and achieving an hourly time scale forecasting accuracy with improvement of 11.7 W/m2.

show abstract

Section: Data Collectionmentioning

confidence: 99%

Enhancing Solar Radiation Forecasting in Diverse Moroccan Climate Zones: A Comparative Study of Machine Learning Models with Sugeno Integral Aggregation

Mendyl,

Demir,

Omar

et al. 2024

Atmosphere

View full text Add to dashboard Cite

show abstract

Statistical study of global solar radiation in the Algerian desert: a case study of Adrar town

Oulimar,

Bouchouicha,

Bailek

et al. 2024

Theor Appl Climatol

View full text Add to dashboard Cite

Rainfall variability and drought in West Africa: challenges and implications for rainfed agriculture

Tefera,

Giovanna Seddaiu,

Alberto Carletti

et al. 2024

Theor Appl Climatol

View full text Add to dashboard Cite

This research investigates rainfall variability and drought patterns in West Africa and their consequential impacts on rainfed agriculture, with a particular focus on vulnerability linked to weather extremes Utilizing NASA POWER/Agro-climatology data, cross-validated against observed meteorological records in the targeted countries, this study spans the years 1981 to 2021, with a particular focus on Ghana and Burkina Faso. The Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and different statistical methods were employed to evaluate the variations in rainfall, including intensity and frequency, as well as analyze drought patterns in the study areas. Despite increased rainfall in the last decade, seasonal and decadal shifts have been noticed, and drought and irregular patterns still threaten the study areas. Temporal analysis reveals fluctuations in temperature and rainfall. SPI and SPEI results indicated a decline in drought frequency, aligned with global trends, though the monthly scale showed no evident decline. The spatial analysis highlights regional variations in rainfall and drought dynamics. The study emphasizes the importance of region-specific mitigation and adaptation strategies, providing valuable insights for informed decision-making in West Africa's agriculture and water resource management under climate change. The findings underscore the continued threat of irregular rainfall patterns and drought, emphasizing the need for tailored approaches to address these challenges.

show abstract

Validation of the Accuracy of NASA Solar Irradiation Data for Four African Regions

Cited by 10 publications

References 0 publications

Enhancing Solar Radiation Forecasting in Diverse Moroccan Climate Zones: A Comparative Study of Machine Learning Models with Sugeno Integral Aggregation

Enhancing Solar Radiation Forecasting in Diverse Moroccan Climate Zones: A Comparative Study of Machine Learning Models with Sugeno Integral Aggregation

Statistical study of global solar radiation in the Algerian desert: a case study of Adrar town

Rainfall variability and drought in West Africa: challenges and implications for rainfed agriculture

Contact Info

Product

Resources

About