AI and Data-Driven Strategies for Control and Optimization of Building Energy Systems

Dear Colleagues,

In the face of escalating energy demands and growing environmental concerns, optimizing energy efficiency in buildings has emerged as a critical challenge. The integration of artificial intelligence (AI) and data-driven strategies offers transformative potential for enhancing building energy systems. This Special Issue, “AI and Data-Driven Strategies for Optimizing Building Energy Efficiency,” explores cutting-edge advancements in leveraging AI and sophisticated data analytics to drive energy performance improvements in buildings.

Traditional approaches to energy management often rely on static models and heuristic methods, which can fall short in addressing the dynamic and complex nature of building operations. With the proliferation of smart sensors and IoT devices, a wealth of real-time data are now available, enabling a shift towards more responsive and adaptive control strategies. AI algorithms, including machine learning and deep learning, are harnessed to analyze these data, uncovering patterns and insights that inform more effective energy management practices.

This Special Issue delves into a range of topics, from the development of predictive models that forecast energy demand and optimize HVAC systems to the implementation of advanced control strategies that minimize energy consumption while maintaining occupant comfort. Contributions will highlight innovative applications of AI technologies, such as neural networks and reinforcement learning, in the context of building energy systems. Additionally, the role of data integration and analytics in facilitating decision-making processes will be examined, showcasing how these tools can drive significant improvements in energy efficiency.

By presenting state-of-the-art research and practical applications, this issue aims to advance the understanding of how AI and data-driven approaches can revolutionize building energy management. It seeks to inspire further research and collaboration in this crucial field, ultimately contributing to more sustainable and energy-efficient built environments.

Dr. Muhyiddine Jradi
Guest Editor

Manuscript Submission Information

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• artificial intelligence (AI)
• data analytics
• energy efficiency
• building energy systems
• smart buildings
• predictive modeling
• machine learning
• HVAC optimization
• real-time data
• adaptive control