RAIL-KNUST will study scenarios and use cases for innovative use of AI and IoTs for Renewable Energy Systems (RES). This will involve the development of; (1) innovative designs for Renewable Energy Systems in farming communities, and (2) methodologies and AI-based solutions for District Energy Shared Systems. Activities in this theme would involve the design and development of AI-enabled cloud platforms, smart ICT controllers, software and virtual layers. This theme will also focus on using AI to design innovative solutions to solve the complexity problems of integrating several renewable energy sources with traditional grids due to the many energy vectors to ensure technological reliability of smart grid networks.
A Hybrid Deep Learning-Based Stochastic Bottom-up Framework for Enhancing Electricity Demand Prediction in Rural Electrification
Julius Adinkrah (PhD)
Machine Learning Model for Predictive Maintenance of Industrial Electrical Machines and Equipment: A Special Case for Distribution Power Transformers
The application of reactive and preventive maintenance strategies to avert transformer failures and safeguard their operations have shown significant limitations in terms of high operational downtimes, over- and under-maintenance issues, maintenance fatigue and revenue loss.
