Lazar Zoran Velimirovic

This paper proposes a data-driven approach to predict session-level EV energy demand using contextual station data and user behavior from historical logs. A large realistic dataset and a time-aware, user-level anti-leakage split ensured robust evaluation. The XGBoost model outperformed other regressors, offering accurate forecasts without vehicle telemetry. This scalable method enables operators to predict energy demand reliably, supporting smarter EV charging network management and allowing future integration of detailed user or technical data.

Serbia, like many other countries, works to manage and protect water resources, promote sustainable practices, and improve infrastructure. However, there is no systematic advocacy for the implementation of sustainable stormwater management practices (SWM) in urban areas in Serbia. As highlighted by the Development Plan of the City of Niš For the Period 2021-2027, in the last decade the number of flash floods has increased, causing infrastructure and property damages. As a result, reducing the risk of flooding by external and internal waters is prioritized under the territorial development and environmental protection axis. As a contribution to the development of SWM practice, in this research, the prediction of the potential of runoff from roofs in the city of Niš, was performed using L-Moment and GIS. The research included 262 buildings in the city center and results indicate an increasing trend in average runoff from roofs across all sectors.

Home energy management systems (HEMS) are increasingly used as a tool that creates optimal consumption and production schedules for Smart Grids, by considering objectives such as energy costs, environmental concerns, load profiles, and consumer comfort. Multiple criteria selection of optimal HEMS seems to be superior to the traditional cost benefit assessment in measuring intangibles and soft impacts, introducing qualitative aspects in the analysis. This paper proposes an algorithm for the selection of optimal HEMS, using the fuzzy AHP method. This methodological framework provides a multi-criteria approach for estimating the benefits and costs of different HEMS within the Smart Grid uncertain environment. This method allows the decision makers to incorporate unquantifiable, asymmetrical, incomplete, nonobtainable information and partially ignorant facts into a decision model. Four criteria and eleven performances for the optimal solution selection are defined. The method is successful in the evaluation of alternatives in the presence of heterogeneous criteria and uncertain environment. The methodology is illustrated on the choice of HEMS from the power distribution company perspective. It is concluded that the evaluation of weighting factors has a decisive character in the choice of the final one of several alternative variants. Fuzzification of input values can also contribute to a more flexible view of the given problem and analysis of sensitivity to various input parameters.

In this work, a new method for identifying the optimal route of electric vehicles based on a hybrid model is offered. A combination of analytical hierarchical method and fuzzy dynamic programming was used to create the hybrid model. The model designed in this manner allows each user of an electric vehicle to decide the ideal route based on their needs. The case study was created using actual data from the city of Nis. The scenarios for two electric vehicle users for the schedule of charging stations and routes for the city of Nis with pre-defined values of travel time, charging time, and the attractiveness of the location are analyzed. Based on the acquired findings, all of the suggested model's advantages can be seen, which are principally reflected in bringing electric vehicle users to the forefront, i.e., harmonizing electric vehicle users' needs on the one hand and travel length and charging time at charging stations on the other.

One way to optimize wastewater treatment system infrastructure, its operations, monitoring, maintenance and management is through development of smart forecasting, monitoring and failure prediction systems using machine learning modeling. The aim of this paper was to develop a model that was able to predict a water pump failure based on the asymmetrical type of data obtained from sensors such as water levels, capacity, current and flow values. Several machine learning classification algorithms were used for predicting water pump failure. Using the classification algorithms, it was possible to make predictions of future values with a simple input of current values, as well as predicting probabilities of each sample belonging to each class. In order to build a prediction model, an asymmetrical type dataset containing the aforementioned variables was used.

The successful automation of a smart home relies on the ability of the smart home control system to organize, process, and analyze different sources of information, according to several criteria. Because of variety of key design criteria that every smart home of the future should meet, the main challenge is the trade-off between them in uncertain environment. In this paper, a problem of smart home design has been solved using the methodology based on multiplicative form of multi-attribute utility theory. Aggregated functions describing different smart home alternatives are compared using stochastic dominance principle. The aggregation of different criteria has been performed through their numerical convolution, unlike usual approach of pairwise comparison, allowing only the additive form of aggregation of individual criteria. The methodology is illustrated on the smart home controller parameter setting.

In this paper, the key performance indicators related to the smart grid efficiency, as the key factor of any energy management system implementation have been analysed. The authors are proposing multi-criteria fuzzy AHP methodology for the determination of overall smart grid efficiency. Four criteria (technology, costs, user satisfaction, and environmental protection) and seven performances (according to EU and US initiatives for analysis of benefits and effects of smart grid systems) for the selection of optimal smart grid project are defined. The analysis shows that the dominant performances of the optimal smart grid project are efficiency, security and quality of supply. The methodology is illustrated on the choice of smart grid development strategy for the medium size power distribution company.