Search

Effective energy management plays a vital role in improving greenhouse (GH) performance and advancing sustainability. This study introduces an innovative multi-objective optimization strategy specifically developed for GH energy management, aiming to reduce reliance on grid energy while maximizing the battery state of charge (SOC) over a defined time period. The optimization framework incorporates decision variables, including network power, battery power, and battery energy, while respecting constraints on battery capacity, initial energy levels, and the minimum and maximum energy limits of the battery storage system. The performance of a smart energy management system (EMS) is assessed by comparing it against conventional optimization techniques. Critical hyperparameters for the optimization process—such as plateau time, prediction time, and optimization time—are identified using the ellipse optimization technique. By modeling the GH as a microgrid, the study examines relevant energy management metrics and load profiles. Simulations performed in MATLAB Simulink (R2021b) reveal substantial improvements, with the smart EMS reducing the objective function by more than 50% relative to traditional EMS approaches. Furthermore, the proposed EMS demonstrates strong robustness under varying load power and irradiation patterns. Even in partial-shading scenarios, the system remains adaptable, with a maximum increase in the objective function of only 0.35553%. Matching the photovoltaic (PV) system output power to realistic field conditions further confirms the EMS’s practical effectiveness. These results highlight the smart EMS’s capability to optimize energy use in GH settings, paving the way for more sustainable energy management solutions. This work advances energy optimization techniques in agricultural contexts, supporting greater resource efficiency and environmental responsibility.

To effectively respond to ongoing global shifts—including globalization, climate change, and population expansion—contemporary urban areas need to integrate digital technologies into their governance and operations. This study explores a framework for urban digitalization, drawing inspiration from the digital twin initiative implemented in Rotterdam. It outlines data-handling approaches suitable for diverse data origins. The discussion also covers associated cybersecurity vulnerabilities and the possible exploitation of smart city infrastructure for military purposes. Finally, by examining publicly accessible datasets on air quality monitoring and electricity use in public lighting, the study compares the functional capabilities of Rotterdam’s digital twin with those of the digital platform developed for Brno. The analysis reveals that each municipality adopts a distinct digital transformation strategy, making a standardized, universal model impractical. Consequently, urban digitalization remains highly context-specific and individualized. At present, both platforms primarily enable the visualization of aggregated urban data. However, neither has yet incorporated sophisticated capabilities, such as artificial intelligence-supported decision-making or predictive modeling of urban phenomena. Nevertheless, they generate valuable datasets that hold significant potential for external stakeholders and third-party applications.