Ale Journal of Sustainable Intelligent Energy Systems

This journal also publishes Open Access Articles

Aims and Scope

Ale Journal of Sustainable Intelligent Energy Systems (AJSIES) aims to promote high-quality research and innovation in the design, development, and optimization of intelligent energy systems. The journal is committed to advancing the integration of smart technologies, data-driven methodologies, and advanced engineering solutions to enhance the efficiency, reliability, and sustainability of modern energy infrastructures. AJSIES seeks to provide a platform for researchers, engineers, and practitioners to explore the transformative role of machine intelligence—including artificial intelligence, machine learning, deep learning, and big data analytics in energy systems. The journal emphasizes the application of these technologies to improve energy generation, distribution, consumption, and management, particularly in the context of smart grids, renewable energy integration, and energy storage solutions. AJSIES aims to support the development of intelligent approaches for energy demand forecasting, predictive maintenance, real-time monitoring, and optimization of power systems. It also encourages contributions that address energy policy, planning, and data-driven decision-making strategies, including transparent pricing models and energy disaggregation techniques. By fostering collaboration between academia and industry, AJSIES intends to bridge the gap between theoretical research and practical implementation. Ale Journal of Sustainable Intelligent Energy Systems is dedicated to supporting global efforts toward clean energy transitions and net-zero targets by enabling innovative, scalable, and efficient solutions. Ultimately, AJSIES aims to contribute to the advancement of next-generation energy systems capable of addressing evolving global energy challenges.

Topics covered include:

• Intelligent energy systems design and optimization
• Smart grids and digital energy infrastructure
• Renewable energy integration and hybrid systems
• AI and machine learning in energy management
• Deep learning for energy forecasting and optimization
• IoT-enabled smart energy monitoring systems
• Big data analytics in energy systems
• Energy storage technologies and optimization
• Predictive maintenance and fault detection in power systems
• Data-driven energy demand and consumption forecasting
• Intelligent control and operation of power systems
• Energy efficiency and cost optimization strategies
• Clean energy transition and net-zero solutions
• Energy policy, planning, and sustainable frameworks
• Next-generation intelligent and resilient energy networks

Editor in Chief : Gümüş Funda Gökçe
Düzce University
turkey