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Journal Aim:
The journal "AI in Sustainable Energy and Environment (AISEE)" aims to be a leading international platform dedicated to fostering innovation at the intersection of artificial intelligence, sustainable energy solutions, and environmental conservation. AISEE seeks to publish high-quality, peer-reviewed research that significantly advances the field by integrating AI technologies to address pressing global challenges in energy and environmental sustainability. Through this journal, we aim to:
Promote interdisciplinary research that combines AI with sustainable energy practices and environmental monitoring.
Encourage the development of cutting-edge technologies that enhance renewable energy integration, improve energy efficiency, and support sustainable urban development.
Provide a forum for discussions on AI-driven solutions for climate change mitigation, disaster response, and policy-making.
Foster a community of researchers, practitioners, and policymakers who are dedicated to using AI for environmental and energy sustainability.
Journal Scope:
1. Optimizing Renewable Energy Integration:
Research on AI applications in forecasting for wind and solar power to optimize grid stability.
Studies on smart grid management systems leveraging AI for improved energy distribution and efficiency.
Development of algorithms for energy storage systems to balance supply and demand dynamically.
2. Enhancing Energy Efficiency:
Exploration of AI in building energy management systems to reduce consumption and enhance efficiency.
Demand response strategies using AI to manage peak loads and reduce overall energy use.
AI-driven predictive models for energy use in industrial, commercial, and residential sectors.
3. Smart Cities and Urban Planning:
Sustainable urban planning through AI, focusing on traffic flow optimization, green spaces, and energy-efficient infrastructure.
Waste management systems enhanced by AI for smarter recycling, composting, and waste reduction strategies.
Urban development models that integrate AI for water management, energy distribution, and public space utilization.
4. Environmental Monitoring and Conservation:
AI technologies for real-time monitoring of wildlife and their habitats to support conservation efforts.
Advanced sensor networks combined with AI for air and water quality monitoring, providing predictive analytics for environmental health.
Remote sensing and AI for tracking deforestation, desertification, and other environmental changes.
5. Predictive Maintenance:
AI algorithms for the predictive maintenance of energy infrastructure like power plants, transmission lines, and utility networks to prevent failures and extend lifespan.
Machine learning models to predict and mitigate energy system failures before they occur.
6. Carbon Footprint Reduction:
Industrial process optimization through AI to lower energy use and reduce emissions.
Research into AI applications for carbon capture, utilization, and storage (CCUS) technologies.
AI-driven tools for assessing and reducing corporate and industrial carbon footprints.
7. Circular Economy and Resource Management:
AI in enhancing material recovery processes, promoting recycling, and reducing waste.
AI for optimizing water resource management, ensuring sustainable use in agriculture, industry, and urban settings.
Supply chain analytics using AI to foster a circular economy by reducing resource consumption.
8. Disaster Response and Climate Change Mitigation:
AI modeling for climate prediction, aiding in proactive measures against climate change impacts.
AI-enhanced systems for rapid response, resource allocation, and recovery operations during natural disasters.
Development of AI solutions for resilience in infrastructure against climate change effects.
9. Sustainable Transportation:
AI optimization for electric vehicle fleets, charging infrastructure, and traffic management.
Public transportation systems enhanced by AI for better scheduling, efficiency, and reduced environmental impact.
10. Data-Driven Policy and Decision Making:
AI tools for environmental policy formulation, implementation, and impact assessment.
Corporate sustainability strategies supported by AI for strategic decision-making.
AI in governance for smarter, data-driven environmental policy-making at local, national, and global levels.
Submission:
AI in Sustainable Energy and Environment encourages:
Original research articles that push the boundaries of AI applications in energy and environmental sustainability.
Review articles that provide a comprehensive overview of emerging trends, critical analysis of current methodologies, and future directions in AI for sustainability.
AISEE is more than just a journal; it is a movement towards a smarter, sustainable future. By focusing on the synergy between AI, energy, and the environment, we aim to not only document progress but to actively contribute to the global effort in combating climate change, enhancing energy efficiency, and promoting sustainable practices. We invite researchers, innovators, and practitioners worldwide to contribute their insights, findings, and visions for a sustainable future through this platform.
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