This page has only limited features, please log in for full access.

Prof. Dr. Cristina Martín
DeustoTech, Faculty of Engineering, University of Deusto, Spain

Basic Info

Basic Info is private.

Research Keywords & Expertise

0 Life Cycle Assessment
0 Quantitative Risk Analysis
0 Sustainable Cities
0 circular economy
0 Uncertainty Assessment

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 23 June 2020 in Sustainable Energy, Grids and Networks
Reads 0
Downloads 0

The daily analysis of loads is one of the most important activities for power utilities in order to be able to meet the energy demand. This analysis not only includes short-term forecasting but it also encompasses the completion of missing load data, known as imputation. In this work we show that adding information of attached or bordering primary substation helps to improve the prediction accuracy in a single substation, since its neighbours may share common weather-related (e.g. temperature, humidity, wind direction, etc.) and human-related (e.g. work-calendar, holidays, cultural consumption patterns, etc.) data. In order to validate these approaches, we test the forecasting and imputation neighbouring methodology on a wide variety of datasets. Results confirm that, given a primary substation, the addition of information from surrounding substations does improve the forecasting and imputation errors.

ACS Style

Cruz E. Borges; Oihane Kamara-Esteban; Tony Castillo-Calzadilla; Cristina Martin Andonegui; Ainhoa Alonso-Vicario. Enhancing the missing data imputation of primary substation load demand records. Sustainable Energy, Grids and Networks 2020, 23, 100369 .

AMA Style

Cruz E. Borges, Oihane Kamara-Esteban, Tony Castillo-Calzadilla, Cristina Martin Andonegui, Ainhoa Alonso-Vicario. Enhancing the missing data imputation of primary substation load demand records. Sustainable Energy, Grids and Networks. 2020; 23 ():100369.

Chicago/Turabian Style

Cruz E. Borges; Oihane Kamara-Esteban; Tony Castillo-Calzadilla; Cristina Martin Andonegui; Ainhoa Alonso-Vicario. 2020. "Enhancing the missing data imputation of primary substation load demand records." Sustainable Energy, Grids and Networks 23, no. : 100369.

Journal article
Published: 22 October 2019 in Environmental Modelling & Software
Reads 0
Downloads 0

Technological advances in the development of tools for modelling and simulating the behaviour and characteristics of urban drainage networks, such as EPA SWMM, represent a great opportunity for managers and infrastructure planners to analyse and evaluate infrastructure resilience against several scenarios. In this sense, the digitalization of existing urban drainage networks becomes an important bottleneck for many small water entities that having the required data, lack the expertise to complete all the preparatory steps before running the simulation. The solution comes hand in hand with developing an appropriate unique tool that enables a seamless integration of heterogeneous data sources and makes simple their comprehension by the implementation of customised and integrated GIS systems that work as a specialist assistant.

ACS Style

Cristina Martin; Oihane Kamara; Iker Berzosa; Jose Luis Badiola. Smart GIS platform that facilitates the digitalization of the integrated urban drainage system. Environmental Modelling & Software 2019, 123, 104568 .

AMA Style

Cristina Martin, Oihane Kamara, Iker Berzosa, Jose Luis Badiola. Smart GIS platform that facilitates the digitalization of the integrated urban drainage system. Environmental Modelling & Software. 2019; 123 ():104568.

Chicago/Turabian Style

Cristina Martin; Oihane Kamara; Iker Berzosa; Jose Luis Badiola. 2019. "Smart GIS platform that facilitates the digitalization of the integrated urban drainage system." Environmental Modelling & Software 123, no. : 104568.