Empirical Study of How Traffic Intensity Detector Parameters Influence Dynamic Street Lighting Energy Consumption: A Case Study in Krakow, Poland

Abstract

Abstract: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.

Revision of Threshold Luminance Levels in Tunnels Aiming to Minimize Energy Consumption at No Cost: Methodology and Case Studies

“…As factor k will be defined by lower values, the new Lth' values should determine a new control switching. In general, the energy savings using traffic intensity detector parameters could end up to 50% [77][78][79][80][81][82][83]. Furthermore, frequent luminance measurements could enhance the energy savings.…”

Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems

“…Over the years, basic control systems have evolved into extremely advanced ones, providing enhanced capabilities to adapt the lighting intensity to the actual operating environment. In contrast to the simplest and old-fashioned LCs, which allow to settle the light intensity only according to a predefined schedule, cutting edge control systems allow to tailor the lumen output to the current vehicular traffic [68], whose amount is monitored by specific sensors, in some cases even taking into account meteorological conditions jointly with measured road surface luminance. Such advanced features are developed in accordance with specific regulations that allow to dynamically reduce the light intensity when specific conditions are met.…”

“…According to case studies investigated in Poland [68], the expected energy saving due to TAI adopting a 15 min interval could reach 47% compared to a preset dimming schedule, which is about 33% of the overall cost per year per each streetlight. Similar results can be found in [79].…”

“…Choice of traffic sensors locations. The third step is the detection of the streets where to place vehicular traffic sensors, which are needed to implement traffic-adaptive energy saving algorithms, such as TAI or FAI [68,79]. As outlined in Section 3.3, significant savings can be achieved by dynamically adapting the light intensity to actual traffic conditions.…”

Graph-Based Spatial Data Processing and Analysis for More Efficient Road Lighting Design

“…To compare the granulation of lighting segments generated using various methods, we have selected the area of a lighting project, carried out by the City of Kraków, Poland, in cooperation with the AGH University of Science and Technology. The project involved replacing almost 4000 lighting fixtures with LED ones, preparing an optimised photometric design and deploying a sensor-based control system to dynamically dim the individual fixtures as required by the standards [31][32][33]. The area in question is presented in Figure 6.…”