Wind Speed Prediction from Site Meteorological Data Using Artificial Neural Network

Ahmed, Khawaja Masood; Khan, Muhammad Arshad; Siddiqui, I. A.; Khan, Siraj Ahmed; Shoaib, Muhammad; Zia, Ibrahim

doi:10.1109/gcwot53057.2022.9772879

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…The neural network achieved an accuracy of 82.5% in predicting the diagnosis of diabetes. In [66] a neural network is used to predict the wind speed to improve wind power generation. The neural network used as inputs the date, air temperature, vapor pressure, and relative humidity, and achieved a high degree of precision and accuracy.…”

Section: Feed Forward Neural Networkmentioning

confidence: 99%

Methods and Techniques for Inter-cloud Data Transfers

Contreras¹

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This thesis focuses on methods and techniques for transferring data from a compute cloud to a storage cloud by using a buffer at the compute cloud. A space driven, a time driven, and a hybrid buffering technique are discussed. It was found that using buffering at the compute cloud is only beneficial when the load on the system is high. To improve performance a threshold-based and a machine learning based adaptive method are introduced at the buffer to dynamically change between buffering techniques at runtime based on the load on the system state. Using the adaptive methods leads to a significant improvement in performance. A proof-of-concept prototype was built for the different data transfer methods and techniques researched. Experiments performed on the prototype provide insights into system behaviour and performance.

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Section: Feed Forward Neural Networkmentioning

confidence: 99%