The lab develops and evaluates agentic AI, LLM-supported diagnostics, and advanced machine-learning workflows through applied case studies in energy informatics, smart buildings, and industrial energy systems.
Case Study 1: Explainable Data Quality Assurance for Smart-Building Energy Analytics
This case study demonstrates how automated data-quality assessment, explainable AI, and lightweight LLM-based reporting can support reliable energy analytics in smart buildings. Using the ADRENALIN smart-building dataset, the study developed a modular pipeline for profiling time-series data, detecting missing values, timestamp irregularities, and statistical anomalies, and aggregating these indicators into a Building Quality Score. SHAP analysis was used to identify which data-quality factors most strongly influenced non-intrusive load monitoring performance, while an LLM module translated diagnostic charts and quality metrics into concise, human-readable explanations. The case study shows how LLM-supported diagnostics can improve transparency, dataset selection, and preprocessing decisions for AI-based energy analytics.
Publication:
Tolnai, B. A., Ma, Z., Jørgensen, B. N., & Ma, Z. G. (2025). An automated domain-agnostic and explainable data quality assurance framework for energy analytics and beyond. Information, 16(10), 836. https://doi.org/10.3390/info16100836
Case Study 2: AI-Based Operational Pattern Discovery in Foundry Melting Processes
This case study applies deep unsupervised learning to discover operational patterns in energy-intensive industrial furnace processes. The study developed TS-IDEC, an image-based convolutional clustering framework that transforms furnace temperature time series into grayscale matrix representations, learns latent features through a deep convolutional autoencoder, and combines soft and hard clustering with a composite internal evaluation metric. Applied to more than 3900 melting operations from a Nordic foundry, the method identified explainable operational modes with different energy consumption, thermal dynamics, and production durations. The case study illustrates how AI can support industrial process diagnostics, energy-efficiency benchmarking, and data-driven operational improvement without requiring labeled process data.
Publication:
Ma, Z., Jørgensen, B. N., & Ma, Z. G. (2025). Discovering operational patterns using image-based convolutional clustering and composite evaluation: A case study in foundry melting processes. Information, 16(9), 816. https://doi.org/10.3390/info16090816