Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks
المؤلفون المشاركون
Tadili, Rachid
Loutfi, Hanae
Bernatchou, Ahmed
Raoui, Younès
المصدر
International Journal of Photoenergy
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-10-11
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
This paper presents three different topologies of feed forward neural network (FFNN) models for generating global, direct, and diffuse hourly solar irradiance in the city of Fez (Morocco).
Results from this analysis are crucial for the conception of any solar energy system.
Especially, for the concentrating ones, as direct component is seldom measured.
For the three models, the main input was the daily global irradiation with other radiometric and meteorological parameters.
Three years of hourly data were available for this study.
For each solar component’s prediction, different combinations of inputs as well as different numbers of hidden neurons were considered.
To evaluate these models, the regression coefficient (R2) and normalized root mean square error (nRMSE) were used.
The test of these models over unseen data showed a good accuracy and proved their generalization capability (nRMSE = 13.1%, 9.5%, and 8.05% and R = 0.98, 0.98, and 0.99) for hourly global, hourly direct, and daily direct radiation, respectively.
Different comparison analyses confirmed that (FFNN) models surpass other methods of estimation.
As such, the proposed models showed a good ability to generate different solar components from daily global radiation which is registered in most radiometric stations.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Loutfi, Hanae& Bernatchou, Ahmed& Raoui, Younès& Tadili, Rachid. 2017. Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks. International Journal of Photoenergy،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1168232
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Loutfi, Hanae…[et al.]. Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks. International Journal of Photoenergy No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1168232
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Loutfi, Hanae& Bernatchou, Ahmed& Raoui, Younès& Tadili, Rachid. Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks. International Journal of Photoenergy. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1168232
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1168232
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر