Indoor air quality (IAQ) is a critical determinant of health, comfort, and productivity, and is strongly connected to building energy demand due to the role of ventilation and air treatment in HVAC systems. This review examines recent applications of Artificial Intelligence (AI) and Machine Learning (ML) for IAQ prediction across residential, educational, commercial, and public environments. Approaches are categorized by predicted parameters, forecasting horizons, facility types, and model architectures. Particular focus is given to pollutants such as CO₂, PM2.5, PM10, VOCs, and formaldehyde. Deep learning methods, especially the LSTM and GRU networks, achieve superior accuracy in short-term forecasting, while hybrid models integrating physical simulations or optimization algorithms enhance robustness and generalizability. Importantly, predictive IAQ frameworks are increasingly applied to support demand-controlled ventilation, adaptive HVAC strategies, and retrofit planning, contributing directly to reduced energy consumption and carbon emissions without compromising indoor environmental quality. Remaining challenges include data heterogeneity, sensor reliability, and limited interpretability of deep models. This review highlights the need for scalable, explainable, and energy-aware IAQ prediction systems that align health-oriented indoor management with energy efficiency and sustainability goals. Such approaches directly contribute to policy priorities, including the EU Green Deal and Fit for 55 package, advancing both occupant well-being and low-carbon smart building operation.

Industrial food production is one of the biggest businesses that supplies most of the food consumed by the world. Despite being one of the largest industries, the lack of appropriate control and quality due to inefficient food management results in a lot of waste and cost ineffectiveness. With the emergence of Internet of Things (IoT) and Industry 4.0, the food industry can be benefited by adapting to these concepts and enhance the desirable quality of the products. In this paper, an advanced quality check method has been proposed by identifying influencing process parameters and proposes a retrofitting architecture for existing machines by implementing a hardware device capable of collecting vast amount of process data and integrating them with a cloud platform for further analysis.

A major benefit of Digital Twins is autonomous decision making. The concept of the Asset Administration Shell (AAS) enables an assets interaction in Industry 4.0 application scenarios and beyond. This article defines and validates implementation possibilities for proactive AASs by integrating their different types in an AAS infrastructure for an order driven production. The proactive AAS execute the VDI/VDE 2193-interaction protocol in a demonstrator. For this purpose suitable AAS submodels for production and storage are modeled.

The residential energy sector contributes substantially to global energy-related emissions. Effective energy management requires an understanding occupant behavior through activity detection and habit identification. Recent advances in artificial intelligence (AI) and machine learning (ML) enable the automatic detection of user activities and prediction of energy needs based on historical consumption data. Non-intrusive load monitoring (NILM) facilitates device-level disaggregation without additional sensors, supporting demand forecasting and behavior-aware control in Home Energy Management Systems (HEMSs). This review synthesizes various AI and ML approaches for detecting user activities and energy habits in HEMSs from 2020 to 2025. The analyses revealed that deep learning (DL) models, with their ability to capture complex temporal and nonlinear patterns in multisensor data, achieve superior accuracy in activity detection and load forecasting, with occupancy detection reaching 95–99% accuracy. Hybrid systems combining neural networks and optimization algorithms demonstrate enhanced robustness, but challenges remain in limited cross-building generalization, insufficient interpretability of deep models, and the absence of dataset standardized. Future work should prioritize lightweight, explainable edge-ready models, federated learning, and integration with digital twins and control systems. It should also extend energy optimization toward occupant wellbeing and grid flexibility, using standardized protocols and open datasets for ensuring trustworthy and sustainability.