Modular building energy management strategy based on a three-level hierarchical model predictive control is proposed in the paper. Building zones, central medium conditioning and microgrid subsystems are controlled independently by individual linear and nonlinear model predictive controllers, and further integrated together as levels of hierarchical coordination control structure based on price-consumption information exchange. The three-level coordination provides a holistic energy management strategy and enables significant demand response ancillary services for buildings as prosumers, while retaining the independence of required expertise in very different building subsystems. The approach is applied for daily operation scheduling of a full-scale building consisting of 248 offices. Models of building subsystems are obtained by identification procedures on measurement data. Compared to rule-based control, detailed realistic simulations show that the overall building operation cost for typical days in summer is reduced by 9-12% for level-by-level energy-optimal and by 15-24% for price-optimal, coordinated operation. The application of predictive control in the proposed way also improves the indoor comfort substantially.

The control system for energy-efficient train operation with the inclusion of a detailed train motion model and train traction system energy efficiency is presented in the paper. A piecewise affine train model is constructed with the parameters obtained for the electromotive train of an industrial manufacturer. The model encompasses intrinsic features of the train system such as linearized resistance force, a set of traction and braking force physical limitations and passengers comfort constraints. The resulting quadratic optimization problem is solved parametrically through dynamic programming giving the off-line precomputed optimal control law that is a function of train speed and traversed path. The on-line computed traction force profile is then tuned with respect to the traction system energy efficiency. The developed control system is evaluated on a detailed real case study scenario put together with a railway operator and the train manufacturer. The presented results show the possibility of significant energy consumption reductions achieved by energy-efficient train control.

Travel time duration and traffic forecasting in urban environments is of critical importance for efficient supply chain logistics and accurate navigation services. The paper compares data-driven statistical and machine learning approaches for urban traffic travel times forecasting on a 15-minute resolution for the next 24 hours. A detailed analysis of the available historical data is performed, including historical floating car data for a one-year period. Data analysis is followed by development and evaluation of baseline, ARIMAX, neural network and gradient boosting models. The predictions are generated on a day-ahead forecasting horizon where the machine learning models are propagated in a recursive strategy. Recursive approach implies more accurate short-term forecasts that are crucial in the context of nowcasting, while maintaining good accuracy on long-term horizons necessary for daily operation planning. The evaluation of the models accuracy is conducted on four main arterial urban routes in the city of Zagreb, Croatia, with travel times data obtained from a commercial navigation service. A detailed analysis of the results is performed, with a special focus put on the accuracy of near future predictions, as well as different models and model structures.

Aphids are significant pests of winter wheat, causing damage by feeding on plant sap and reducing crop yield and quality. This study evaluates the potential of hyperspectral remote sensing (350–2500 nm) and machine learning (ML) models for classifying healthy and aphid-infested wheat canopies. Field-based hyperspectral measurements were conducted at three growth stages—T1 (stem elongation–heading), T2 (flowering), and T3 (milky grain development)—with infestation levels categorized according to established economic thresholds (ET) for each growth stage. Spectral data were analyzed using Uniform Manifold Approximation and Projection (UMAP); vegetation indices; and ML classification models, including Logistic Regression (LR), k-Nearest Neighbors (KNNs), Support vector machines (SVMs), Random Forest (RF), and Light Gradient Boosting Machine (LGBM). The classification models achieved high performance, with F1-scores ranging from 0.88 to 0.99, and SVM and RF consistently outperforming other models across all input datasets. The best classification results were obtained at T2 with an F1-score of 0.98, while models trained on the full spectrum dataset showed the highest overall accuracy. Among vegetation indices, the Modified Triangular Vegetation Index, MTVI (r_pb = −0.77 to −0.82), and Triangular Vegetation Index, TVI (r_pb = −0.66 to −0.75), demonstrated the strongest correlations with canopy condition. These findings underscore the utility of canopy spectra and vegetation indices for detecting aphid infestations above ET levels, allowing for a clear classification of wheat fields into “treatment required” and “no treatment required” categories. This approach provides a precise and timely decision making tool for insecticide application, contributing to sustainable pest management by enabling targeted interventions, reducing unnecessary pesticide use, and supporting effective crop protection practices.