Szczegóły publikacji
Opis bibliograficzny
Estimation of road lighting power efficiency using graph-controlled spatial data interpretation / Sebastian ERNST, Leszek KOTULSKI // W: Computational Science – ICCS 2021 : 21st international conference : Krakow, Poland, June 16–18, 2021 : proceedings, Pt. 1 / eds. Maciej Paszyński, [et al.]. — Cham : Springer Nature Switzerland, cop. 2021. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 12742. Theoretical Computer Science and General Issues ; ISSN 0302-9743). — ISBN: 978-3-030-77960-3; e-ISBN: 978-3-030-77961-0. — S. 585–598. — Bibliogr., Abstr. — Publikacja dostępna online od: 2021-06-09
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 134703 |
|---|---|
| Data dodania do BaDAP | 2021-06-23 |
| DOI | 10.1007/978-3-030-77961-0_47 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2021 |
| Czasopisma/serie | Lecture Notes in Computer Science, Theoretical Computer Science and General Issues |
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.
