Abstract
Estimation of street lighting energy requirements is a task crucial for both investment planning and efficiency evaluation of retrofit projects. However, this task is time-consuming and infeasible when performed by hand. This paper proposes an approach based on analysis of the publicly available map data. To assure the integrity of this process and automate it, a new type of graph transformations (Spatially Triggered Graph Transformations) is defined. The result is a semantic description of each lighting situation. The descriptions, in turn, are used to estimate the power necessary to fulfil the European lighting standard requirements, using pre-computed configurations stored in a ‘big data’ structure.
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Notes
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The use of this term may be confusing, at it is often used to describe parallel execution of logic on separate processors in concurrent applications. Please note that in this paper, it is always used to denote geometric parallelism of shapes.
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Please note that neither the algorithms used to detect the spatial relationships nor those used to estimate the road width are not presented here in detail. They are, however, a subject of individual research tracks; their results will be published in future papers.
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The estimation of power needed to illuminate them cannot be based on their length (W/m), but rather on their area. Its accuracy may be lower, but illumination of irregular areas constitutes a small percentage of the overall power consumption by city lighting.
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This value is stored to represent the susceptibility of the required power to parameter value fluctuations, which in turn can be used to estimate whether a certain power value is likely to occur in real-life situations, or if a greater margin should be assumed to make the estimate more realistic.
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Ernst, S., Kotulski, L. (2021). Estimation of Road Lighting Power Efficiency Using Graph-Controlled Spatial Data Interpretation. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12742. Springer, Cham. https://doi.org/10.1007/978-3-030-77961-0_47
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