@inproceedings{5542489bec004a6ab0fb87f43af17f6e,
title = "Forecasting Solar Irradiance using Hybrid Stationary Wavelet Transform- Quaternion Valued Neural Network with a Softplus AMSGrad Learning Algorithm",
abstract = "Efficient use of solar energy requires reliable forecasting values. In this paper, we propose a forecasting system combining the stationary wavelet transform and quaternion-valued neural networks (SWT-QVNN) and use it to forecast solar irradiance. In addition, a quaternion variant of the softplus AMSGrad learning algorithm is developed and used in optimizing the developed neural network. The proposed system was tested using irradiance data from two cities Abu Dhabi, UAE, and Tamanrasset, Algeria. It did reduce Root Mean Squared Error (RMSE) by more than 60\% compared to the baseline model that uses a real-valued neural network.",
keywords = "AMSGrad algorithm, Forecasting, Quaternion Neural Network, Solar irradiance",
author = "Saoud, \{Lyes Saad\} and Hasan Almarzouqi",
note = "Funding Information: This work has been funded by the ICT Fund, Telecommunications Regulatory Authority (TRA), PO. Box: 26662 Abu Dhabi, United Arab Emirates. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Power Systems Technology, POWERCON 2022 ; Conference date: 12-09-2022 Through 14-09-2022",
year = "2022",
doi = "10.1109/POWERCON53406.2022.9929612",
language = "British English",
series = "2022 IEEE International Conference on Power Systems Technology: Embracing Advanced Technology in Power and Energy Systems for Sustainable Development, POWERCON 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Haroon Rashid",
booktitle = "2022 IEEE International Conference on Power Systems Technology",
address = "United States",
}