Sustainable Street Lighting Design Supported by Hypergraph-Based Computational Model

Street lighting systems are significant energy consumers in urban environments. The important step toward the reduction of this energy consumption and, thus, finding a balance between functional requirements and savings-related demands, was introducing LED-based light sources. There still exists, however, a margin for further savings, which is associated with well-tailored designs of road lighting installations. The critical impediment that has to be overcame beforehand is the computational complexity related to preparing such a well-suited design. To make this approach feasible, we propose using the formal graph-based model, enabling efficient heuristic computations. In this article, we demonstrate several real-life cases showing a coarse estimation of potential savings in terms of reduced CO2 emission. The presented results are expressed in kWh of saved energy, metric tones of CO2 , but also as a volume of combusted fuels, to make the assessment more tangible.

[1]  Michel Wermelinger,et al.  A graph transformation approach to software architecture reconfiguration , 2002, Sci. Comput. Program..

[2]  Marc Holze,et al.  Defining Abstract Graph Views as Module Interfaces , 2008, AGTIVE.

[3]  Adam Sedziwy On Acceleration of Multi-Agent System Performance in Large Scale Photometric Computations , 2013, KES-AMSTA.

[4]  Jens H. Weber-Jahnke Modelling of Longitudinal Information Systems with Graph Grammars , 2008 .

[5]  Jens H. Weber Modelling of Longitudinal Information Systems with Graph Grammars , 2007, AGTIVE.

[6]  Joost Engelfriet,et al.  Node Replacement Graph Grammars , 1997, Handbook of Graph Grammars.

[7]  Leszek Kotulski,et al.  GRADIS - The multiagent environment supported by graph transformations , 2010, Simul. Model. Pract. Theory.

[8]  Miomir Kostic,et al.  Recommendations for energy efficient and visually acceptable street lighting , 2009 .

[9]  Adam Sędziwy,et al.  Representation of Objects in Agent-Based Lighting Design Problem , 2013 .

[10]  Leszek Kotulski,et al.  Distributed Adaptive Design with Hierarchical Autonomous Graph Transformation Systems , 2007, International Conference on Computational Science.

[11]  Ferdinando Salata,et al.  Energy Optimization of Road Tunnel Lighting Systems , 2015 .

[12]  Konrad Kulakowski,et al.  Heuristic Rating Estimation Approach to The Pairwise Comparisons Method , 2013, Fundam. Informaticae.

[13]  Reiko Heckel,et al.  Algebraic Approaches to Graph Transformation - Part II: Single Pushout Approach and Comparison with Double Pushout Approach , 1997, Handbook of Graph Grammars.

[14]  Rubino Geiß,et al.  Graph Rewriting for Hardware Dependent Program Optimizations , 2007, AGTIVE.

[15]  Adam Sędziwy,et al.  A New Approach to Street Lighting Design , 2016 .

[16]  Hartmut Ehrig,et al.  Handbook of graph grammars and computing by graph transformation: vol. 3: concurrency, parallelism, and distribution , 1999 .

[17]  Leszek Kotulski,et al.  Multi-Agent System for Distributed Adaptive Design , 2011 .

[18]  Oar,et al.  Greenhouse Gas Equivalencies Calculator , 2015 .

[19]  Josef Kittler,et al.  Applications of Graph Transformations with Industrial Relevance , 2011, Lecture Notes in Computer Science.