Guest Editors
Prof. Upaka Rathnayake
Email: upaka.rathnayake@atu.ie
Affiliation: Faculty of Engineering and Design, Atlantic Technological University, Ireland
Homepage: https://pure.atu.ie/en/persons/upaka-rathnayake/
Research Interests: renewable energy, SDG7, forecasting against climate action, explainable artificial intelligence
Summary
This Special Issue aims to explore recent advances in artificial intelligence (AI) for renewable energy forecasting, with a focus on improving the accuracy, reliability, and interpretability of predictive models. It will highlight innovative AI methodologies, including machine learning, deep learning, hybrid models, and explainable artificial intelligence, for forecasting renewable energy resources such as solar, wind, hydropower, etc. The issue will also address key challenges such as data scarcity, climate variability, model uncertainty, and integration with environmental and hydrological systems. By bringing together interdisciplinary research, the Special Issue seeks to promote the development of robust AI-driven forecasting frameworks that support sustainable energy planning and climate-resilient renewable energy systems.
Suggested themes but not limited to:
· AI-Based Forecasting of Climate-Dependent Renewable Energy
· Hydropower Generation Forecasting Using Artificial Intelligence
· Explainable Artificial Intelligence (XAI) for Renewable Energy Models
· Climate Change and Renewable Energy Variability Analysis
· Hybrid AI and Hydrological Modeling Approaches for Reservoir Management
· Multi-Objective Optimization in Renewable Energy Systems
· Data-Driven Forecasting in Data-Scarce Regions
Keywords
artificial intelligence, renewable energy forecasting, climate variability and energy systems, energy system forecasting, sustainable energy planning, smart energy systems
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