Artificial neural network based simplified one day ahead forecasting of solar photovoltaic power generation
المؤلفون المشاركون
Khattak, Abraiz
Khan, M. Adam
Munir, Muhammad Asim
Imran, Kashif
Ulasyar, Abasin
Ullah, Nasim
Ul Haq, Azhar
المصدر
Journal of Engineering Research
العدد
المجلد 10، العدد 1 A (31 مارس/آذار 2022)، ص ص. 175-189، 15ص.
الناشر
جامعة الكويت مجلس النشر العلمي
تاريخ النشر
2022-03-31
دولة النشر
الكويت
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
The intermittency of solar energy resources possesses a serious challenge in balancing the power generation and load demand.
To enhance the consistency of the system, it is crucial to forecast solar photovoltaic power.
Among numerous techniques, Artificial Neural Network (ANN) is an efficient tool that may help simplify this problem.
In this study, all 63 combinations of six input parameters, i.e., temperature, dew point, wind speed, cloud cover, relative humidity, and pressure, were applied one by one to ANN to forecast 24 hours ahead PV generation.
The power forecast results were obtained based on weather forecast data of 21 days sampled from the recorded forecasted data of 180 days.
To quantify the error between predicted and measured solar PV generation, Root Mean Squared Error (RMSE) was used, and the results of different input combinations were compared on basis of this statistical matrix.
The analysis showed that the generation is best predicted on two combinations : the first is comprising of temperature, dew point, relative humidity, and cloud cover, while the second consists of all six parameters.
And some of the combinations consisting of three parameters also resulted in RMSEs in close proximity of the least error value.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Munir, Muhammad Asim& Khattak, Abraiz& Imran, Kashif& Ulasyar, Abasin& Ullah, Nasim& Ul Haq, Azhar…[et al.]. 2022. Artificial neural network based simplified one day ahead forecasting of solar photovoltaic power generation. Journal of Engineering Research،Vol. 10, no. 1 A, pp.175-189.
https://search.emarefa.net/detail/BIM-1495021
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Munir, Muhammad Asim…[et al.]. Artificial neural network based simplified one day ahead forecasting of solar photovoltaic power generation. Journal of Engineering Research Vol. 10, no. 1 A (Mar. 2022), pp.175-189.
https://search.emarefa.net/detail/BIM-1495021
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Munir, Muhammad Asim& Khattak, Abraiz& Imran, Kashif& Ulasyar, Abasin& Ullah, Nasim& Ul Haq, Azhar…[et al.]. Artificial neural network based simplified one day ahead forecasting of solar photovoltaic power generation. Journal of Engineering Research. 2022. Vol. 10, no. 1 A, pp.175-189.
https://search.emarefa.net/detail/BIM-1495021
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 187-189
رقم السجل
BIM-1495021
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