This paper introduces a novel measure to quantify structural information in hierarchical graphs. It addresses the limitation of current methods that do not adequately account for hierarchical structures. By considering inner structural information and distinguishability of higher-level vertices, the proposed measure captures the additional information generated by the hierarchy. The hypothesis that hierarchical graphs contain more structural information is validated using the “Countries” dataset. The results demonstrate a measurable increase in the information content when the hierarchical structure is considered, compared to a simple graph representation. This highlights the importance of recognizing and utilizing hierarchy to enhance the informational richness of graphs, potentially improving the performance of graph-based machine learning models.

Computational methods play a significant role in reducing energy consumption in cities. Many different sensor networks (e.g., traffic intensity sensors, intelligent cameras, air quality monitoring systems) generate data that can be useful for both efficient management (including planning) and reducing energy usage. Street lighting is one of the most significant contributors to urban power consumption. This paper presents a summary of recent attempts to use computational methods to reduce energy usage by lighting systems, with special focus on graph-based methods. Such algorithms require all the necessary data to be integrated, in order to function properly: this task is not trivial, and is very time-consuming; therefore, the second part of the paper proposes a novel approach to integrating urban datasets and automating the optimisation process. In two practical examples, we show how spatially triggered graph transformations (STGT) can be used to build a model based on the road network map, sensor locations and street lighting data, and to introduce semantic relations between the objects, including utilisation of existing infrastructure, and planning of development to maximise efficiency.

This paper presents a graph-based approach to modelling and analysis of spatial (GIS) datasets supporting the deployment of smart city solutions. The presented approach is based on the spatially-triggered graph transformations (STGT) methodology, which allows for materialisation of spatial relationships detected using suitable tools, as well as performing measurements and modifications of geometries. The theory is illustrated using a real-world example which concerns street lighting. It shows how an existing traffic sensor network can be used to enable dynamic dimming of lamps, which can result in significant energy usage savings. Also, network analysis is applied to broaden the coverage of such systems, even in case of sensor sparsity. The presented results have been obtained in a real-world project and are due for larger-scale validation in the near future.

Streszczenie: W artykule przedstawiono historię rozwoju i wykorzystania mechanizmu transformacji grafowych w ramach prac grupy badawczej GRADIS w latach 2006-2022 na Wydziale

Contemporary risk management is based on statistical analysis. Such an approach has a few crucial disadvantages. First of all, it has limited applicability to new technological solutions. In this paper, a new idea for risk evaluation and management is put forward. The proposed approach is based on the autonomous systems theory. The theoretical foundation of the proposed idea is described and its prospective applications are discussed. The proposed measures of risk are based on the idea of the controllability of the system—the greater the level of controllability, the lower the risk. Various aspects of controllability are analyzed—economic, technological, and industrial. For each aspect of controllability, the problem of defining adequate measures for the level of risk is discussed. The proposed approach allows the risk assessor to analyze the system deeply. As a consequence, the analyst can assess the risk based not only on a posteriori statistics but also on an analysis of the crucial properties of the system. This allows the investigator to predict a priori possibilities of critical events. The proposed methodology is applied to the power industry.

More and more street lighting deployments use LED technology as a light source. Unfortunately, the new technology also brings some challenges with it that remain unnoticed until installed at scale. This article presents issues related to capacitive reactive power consumed by LED luminaires. The problem is even more profound if the luminaire is dimmed, because it consumes capacitive reactive power, which is very undesirable in the power system. Countermeasures in terms of reactive power compensation for a luminaire working with variable power and their effects are also presented. The article also contains the results of the harmonic analysis of the LED luminaires current for full power and dimmed operation.

The use of formal methods and artificial intelligence has made it possible to automatically design outdoor lighting. Quick design for large cities, in a matter of hours instead of weeks, and analysis of various optimization criteria enables to save energy and tune profit stream from lighting retrofit. Since outdoor lighting is of a large scale, having luminaires on every street in urban areas, and since it needs to be retrofitted every 10 to 15 years, choosing proper parameters and light sources leads to significant energy savings. This paper presents the concept and calculations of Levelized Cost of Electricity for outdoor lighting retrofit. It is understood as cost of energy savings, it is in the range from 23.06 to 54.64 EUR/MWh, based on real-world cases. This makes street and road lighting modernization process the best green “energy source” if compared with the 2018 Fraunhofer Institute cost of electricity renewable energy technologies ranking. This indicates that investment in lighting retrofit is more economically and ecologically viable than investment in new renewable energy sources.

LED-based street lighting installations generate reactive power, particularly when they are dynamically dimmed. It contributes to power loss and efficiency reduction of the grid. The reactive power can be compensated by installing additional dynamically connected inductors in lighting control cabinets. However such an approach significantly increases the cost of the lighting infrastructure. The goal of this paper is to propose another, low cost approach to reactive power compensation for dynamically dimmed lighting installations. It is based on connecting fixed settings inductors at lighting control cabinets. The inductors settings are calculated by the proposed algorithm for city-scale lighting systems. Its objective is to completely eliminate capacitive reactive power and to keep inductive reactive power within acceptable limits.

In this paper, we present a comparative review of the externalities of electricity production. First of all, the environmental impact is considered. A discussion of the influence of various electricity production processes on human health follows. The studies are conducted in the context of historical development. Current trends, as well as a historical background that resulted in the changes that can be observed today, are presented. The considerations are supported by a few case studies. Analysis of perspectives for the development of electricity generation methods, in particular the indication of clean energy sources and the perspectives of their exploitation, is the main aim of this paper.

LED-based street lighting installations generate reactive power, particularly when they are dynamically dimmed. It contributes to power loss and efficiency reduction of the grid. The reactive power can be compensated by installing additional dynamically connected inductors in lighting control cabinets. However such an approach significantly increases the cost of the lighting infrastructure. The goal of this paper is to propose another, low cost approach to reactive power compensation for dynamically dimmed lighting installations. It is based on connecting fixed settings inductors at lighting control cabinets. The inductors settings are calculated by the proposed algorithm for city-scale lighting systems. Its objective is to completely eliminate capacitive reactive power and to keep inductive reactive power within acceptable limits.

The use of LED technology for street lighting, due to its obvious advantages, is now becoming very widespread. Unfortunately, the new technology also brings with it problems that users often don't realize until they install a new type of lighting. This article presents the problem of capacitive reactive power consumed by LED fixtures. Also presented are the effects of using reactive power compensation for a luminaire working with variable power

Concentrated solar power plants belong to the category of clean sources of renewable energy. The paper discusses the possibilities for the use of molten salts as storage in modern CSP plants. Besides increasing efficiency, it may also shift their area of application: thanks to increased controllability, they may now be used not only to cover baseload but also as more agile, dispatchable generators. Both technological and economic aspects are presented, with focus on the European energy sector and EU legislation. General characteristics for CSP plants, especially with molten salt storage, are discussed. Perspectives for their development, first of all in economic aspects, are considered.

This paper presents a comparative study of differences in energy consumption while applying 2004 and 2014 releases of the CEN/TR 13201 standard for lighting designs. Street lighting optimal design and its optimization is discussed. To provide a reliable comparison, optimal designs for a given representative set of streets were calculated. The optimization was performed by newly developed software. As a test bed, a set of streets was selected with varying physical and traffic characteristics. The energy consumption was measured on the same set of streets both statically, which assumed the same lighting levels throughout night, and with a dynamic control, which adjusted lighting based on traffic intensity. For experiments with the dynamic control, one year of traffic intensity data were used. The findings confirm increased economical impact of dynamic control for the 2014 standard, which results in significant energy saving.

Large-scale photometric computations carried out in the course of lighting design preparation were already subject of numerous works. They focused either on improving the quality of design, for example related to energy-efficiency, or dealt with issues concerning the computation complexity and computations as such. However, mutual influence of the design process and dynamic dimming of luminaires has not yet been addressed. If road segments are considered separately, suboptimal results can occur in places such as junctions. Considering the entire road network at once complicates the computation procedures and requires additional processing time. This paper focuses on a method to make this more efficient approach viable by applying reversed scheme of design and control. The crucial component of both design and control modules is data inventory which role is also discussed in the paper.

Roadway lighting retrofit is a process continuously developed in urban environments due to both installation aging and technical upgrades. The spectacular example is replacing the high intensity discharge (HID) lamps, usually high pressure sodium (HPS) ones, with the sources based on light-emitting diodes (LED). The main focus in the related research was put on energy efficiency of installations and corresponding financial benefits. In this work, we extend those considerations analyzing how lighting optimization impacts greenhouse gas (GHG) emission reduction and what are the resultant financial benefits expressed in terms of emission allowances prices. Our goal is twofold: (i) obtaining a quantitative assessment of how a GHG footprint depends on a technological scope of modernization of a city HPS-based lighting system; and (ii) showing that the costs of such a modernization can be decreased by up to 10% thanks to a lowered CO 2 emission volume. Moreover, we identify retrofit patterns yielding the most substantial environmental impact.

The deployment of dynamic street lighting, which adjusts lighting levels to fulfill particular needs, leads to energy savings. These savings contribute to the overall lighting infrastructure maintenance cost. Yet another contribution is the cost of traffic intensity data. The data is read directly from sensor systems or intelligent transportation systems (ITSs). The more frequent the readings are, the more costly they become, because of hardware capabilities, data transfer and software license costs, among others. The paper investigates a relationship between the frequency of readings, in particular the averaging window size and step, and achieved energy savings. It is based on a simulation, taking into account a representative part of a city and traffic intensity data, which span over a period of one year. While the energy consumption reduction is simulated, all data, including each luminaire power setting, induction loop locations and street characteristics, come from a representative sample of the city of Krakow, Poland. Controlling the power settings complies with the lighting standard CEN/TR 13201. Analysis of the outcomes indicates that the shorter the window size or step are, the more energy saving that is available. In particular, for the previous standard CEN/TR 13201 2004, having the window size and step at 15 min results in 26.75% of energy saving, while reducing these values to 6 min provides 27%. Savings are more profound for the current standard (CEN/TR 13201 2014), assuming a 15 min size and step results in 47.43%, while having a 6 min size and step provides 47.69%. The results can serve as a guideline for identifying the economic viability of dynamic lighting control systems. Additionally, it can be observed that the current lighting standard provides far greater potential for dynamic control then the previous standard.

The paper discusses results of a pilot project aimed at providing dynamic control for 4,000 LED luminaires in Kraków, Poland. The main research goal is to provide a flexible and highly scalable system architecture. Thus, the work regards both theoretical model improvements, comparative technology research, and architectural flexibility assessment based on available third-party systems that must be interacted with. The structure of the system is presented in detail, along with relation to previously-published theoretical work, technologies used and important implementation details. The underlying theoretical concepts, based on the dual graph grammars, are also presented. A critical discussion presents the benefits of the proposed solution in the light of various practical problems encountered during implementation of the pilot project.

The paper introduces a definition of dual graph grammar. It enables two graphs to share information in a synchronized way. A smart city example application, which is an outdoor lighting control system utilizing the dual graph grammar, is also demonstrated. The system controls dimming of street lights which is based on traffic intensity. Each luminaire's light level is adjusted individually to comply with the lighting norms to ensure safety. Benefits of applying the dual graph grammar are twofold. First, it increases expressive power of the mathematical model that the system uses. It becomes possible to take into account complex geographical distribution of sensors and logical dependencies among them. Second, it increases the system's efficiency by reducing the problem size during run-time. Experimental results show a reduction of the computation time by a factor of 2.8. The approach has been verified in practice.

The lighting standards allow to dim the lighting when the road traffic decreases. A control system gathers information from sensors and generates proper dimming levels for lighting points. The Dual Graph Grammars has been proposed as a formal background to maintain the information structure for such a control system. It results in separation of sensors structure from lighting infrastructure. It enables taking into account complex geographical distribution of sensors and logical dependencies among them, which leads to more precise and energy efficient control. What is more important it decreases the control system’s computing power requirements by reducing the problem size during run-time. The approach has been verified in practice by deployment to a control system which manages 3,768 light points. Experimental results show a reduction of the computation time by a factor of 2.8 in this case and quickly grows when number of sensors increases. It makes the control system to be scalable in IoT environments.

In this paper, the problem of short-term prediction of traffic flow in a city traffic network is considered. This prediction is performed in order to provide input data to a dynamic control system for street lighting. The forecasting is done by a multi-layer using artificial neural network. Because of the limited number of sensors, the data is insufficient to describe the relation between the traffic intensity at a given point and the points in which the flow intensity is measured. The proposed approach is tested by using data from the centre of Kraków. The prediction error turned to be low.