The available position is for a Research Assistant to work on a research project entitled “A Novel Deep Learning Model for Accurate Solar Energy Forecast in Future Smart Grids: Towards Intelligent and Sustainable Smart Grid Networks” funded by Ministry of Higher Education, Research and Innovation of Oman under grant no. BFP/RGP/ICT/23/ under agreement no. MoHERI/BFP/ASU/2023.

To assist the Principal Investigator (PI) of the project in achieving the following objectives: 

1) To review and investigate the performance of various existing solar energy models using a comprehensive set of key performance indicators. 

2) To develop a new deep learning architecture based on power and energy estimation (NDA) for predicting PV power, accurate assessment and forecasting of solar energy resources considering all important parameters for different climatic conditions. The proposed architecture aims to improve the accuracy of solar energy forecasting, which is essential for the effective integration of solar energy into future smart grids.

 3) To gather information about different global weather climates and use it to train a new deep learning model to predict the capacity of large-scale distributed solar energy systems. Using a variety of weather data, the model aims to improve the accuracy of solar power forecasting in different regions and climates.

 4) To optimize the parameters of the proposed NDA model using the Bayesian Optimization technique, achieving accuracy and fast convergence of the proposed NDA model. 

5) To predict the capacity and state of charge of a Battery Energy Storage System (BESS) during seasonal transitions. This model will consider various factors such as weather patterns, load demand, and other relevant parameters to achieve accurate predictions. The results of this study will provide insights into the effectiveness of the proposed model for optimizing the performance of BESS in future smart grids. 

6) To demonstrate the potential of the proposed model as a useful tool in the energy market for future energy exchange concept in the smart grid and also to effectively manage the output energy of large clusters of distributed solar energy PV systems using the proposed NDA model. 

7) To help with Principal investigator (PI) for writing the research articles and reviewing etc

The candidate must have a strong research background in the area of Electrical/Electronic/Renewable Energy (Solar) engineering. Having a relevant strong publication record is required. A knowledge on the area of renewable energy (solar energy), Matlab, python and machine learning algorithms is mandatory.

The candidate must have the following skills and attributes: 1) Excellent background in the area of renewable energy (solar energy). 2) Excellent knowledge in the area of prediction solar model. 3) Excellent programming (Matlab coding, python coding etc.) skills. 4) Excellent analytical and critical thinking skills. 5) Excellent academic writing skills. 6) Excellent presentation and communication skills. 7) Proficiency in using MS Word or LaTeX editors. 8) Excellent in MS Visio software. 9) Excellent in Matlab Software’s (Simulink). 10) Excellent machine learning algorithms during Simulink environments. 11) Self-motivation with minimal supervision

PhD in the field of Electrical/Electronic/Renewable Energy (Solar) or any related discipline.