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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Andrade, M. A. P. & Teodoro, M. F. (2026). Modeling the service quality indicators TIEPI and END using quantile regression. In Osvaldo Gervasi, Beniamino Murgante, Chiara Garau, Yeliz Karaca, Maria Noelia Faginas Lago, Francesco Scorza, Ana Cristina Braga (Ed.), Computational science and its applications: ICCSA 2025 Workshops. (pp. 152-163). Praga: Springer Nature.
Exportar Referência (IEEE) 
M. A. Andrade and M. F. Teodoro,  "Modeling the service quality indicators TIEPI and END using quantile regression", in Computational science and its applications: ICCSA 2025 Workshops, Osvaldo Gervasi, Beniamino Murgante, Chiara Garau, Yeliz Karaca, Maria Noelia Faginas Lago, Francesco Scorza, Ana Cristina Braga, Ed., Praga, Springer Nature, 2026, pp. 152-163
Exportar BibTeX 
@inproceedings{andrade2026_1782190827821,
	author = "Andrade, M. A. P. and Teodoro, M. F.",
	title = "Modeling the service quality indicators TIEPI and END using quantile regression",
	booktitle = "Computational science and its applications: ICCSA 2025 Workshops",
	year = "2026",
	editor = "Osvaldo Gervasi, Beniamino Murgante, Chiara Garau, Yeliz Karaca, Maria Noelia Faginas Lago, Francesco Scorza, Ana Cristina Braga",
	volume = "",
	number = "",
	series = "",
	doi = "10.1007/978-3-031-97596-7_10",
	pages = "152-163",
	publisher = "Springer Nature",
	address = "Praga",
	organization = "",
	url = "https://link.springer.com/book/10.1007/978-3-031-97596-7"
}
Exportar RIS 
TY  - CPAPER
TI  - Modeling the service quality indicators TIEPI and END using quantile regression
T2  - Computational science and its applications: ICCSA 2025 Workshops
AU  - Andrade, M. A. P.
AU  - Teodoro, M. F.
PY  - 2026
SP  - 152-163
SN  - 0302-9743
DO  - 10.1007/978-3-031-97596-7_10
CY  - Praga
UR  - https://link.springer.com/book/10.1007/978-3-031-97596-7
AB  - This study investigates the continuity of electricity distribution service in mainland Portugal using quantile regression models applied to two key technical indicators: TIEPI (Time of Interruption Equivalent to Installed Power) and END (Energy Not Delivered). Based on data from E-REDES (.2014–.2022), the analysis evaluates how interruption frequency and duration, measured by SAIFI (System Interruption Average Frequency Index) and SAIDI (System Interruption Average Duration
Index), affect energy supply resilience across different service quality zones and municipalities. Results show that SAIDI has a consistently positive and increasing effect on both END and TIEPI across quantiles, especially at the uppermost, indicating higher exposure to supply loss in the
Municipality Codes with po or performance. Fixed effects highlight considerable municipal level heterogeneity, while QSR zone classifications alone do not significantly explain performance after accounting for local variation. These findings support the need for geographically target investment
and regulation, and demonstrate the value of distribution sensitive modeling in infrastructure performance assessment.
ER  -