The increasing demand for the energy-efficient and occupant-centred operation of educational buildings requires accurate and interpretable models capable of predicting indoor environmental conditions under real operating constraints. This study proposes a residual learning-enhanced grey-box modelling framework for predicting indoor air temperature and assessing indoor environmental quality indicators in a KNX-enabled educational building operating under simple thermostatic heating control. The approach combines a reduced-order discrete-time RC thermal model with a data-driven machine learning component trained to model the next-step residual between measured and simulated indoor temperatures. High-resolution KNX monitoring data were recorded at a 5 min sampling interval over three consecutive months (October–December) during the heating season. Using a chronological 70/30 train–test split, the identified RC grey-box model achieved a pooled test RMSE of 0.269 °C, an MAE of 0.126 °C, and an R² of 0.987. The proposed hybrid formulation achieved RMSE = 0.343 °C, MAE = 0.106 °C, and R² = 0.978 across 62,456 test samples. While the pooled RMSE remains influenced by occasional larger deviations in a small number of rooms, the hybrid model yields a consistent reduction in absolute error (≈16% MAE reduction) and reduced inter-room variability compared to the physics-based baseline. These results indicate that residual learning can enhance predictive robustness under decentralized thermostatic operation and limited sensing, while preserving physical interpretability. The proposed framework provides a practical and scalable solution for indoor temperature prediction and IEQ assessment in educational buildings using existing KNX automation data.

The paper presents a case study for the evaluation and comparison of three calculation methods in terms of taking the water flows resulting from storms and after melting snow for a metropolitan area, whose rainwater catchment system is the most often completely over run, with frequent flooding in the area. The study compares the results of three calculation methods by correlating the relationships and values used in the literature with the real situation determined on several calculation sections, on different water courses in the proposed area. Based on comparisons for the obtained results, the paper presents a series of conclusions that could be useful in urban systems projects.

The protection of the water sources represents a topic that concerns humanity worldwide. Hence, the decrease of the water losses that occurs in water distribution networks during its overall operational lifetime, is an important issue for water operators and for researchers at theoretical and experimental level. Over the last years progresses have been made on the evaluation and analysis methods in order to eliminate the water losses. Among these methods, the evaluation of the water distribution systems, made on performance indicators by the use of the water balance, is the most current. Thus, the calculated value of each performance indicator and the analysis of these values will lead to the identification of the real problems within the water distribution system. As a result, this analysis has to be the first step in the planning phase of a new project and in the same time, it can serve as a pattern for any water distribution systems in order to decrease the water losses at lower costs.