Enhancing Climate Resilience Through Urban Microscale Weather Data Analysis
September 5th 2024
Author and presenter: Arnau Comas
Collaborators: Jose Manuel Broto, Maite Sellart, Gerard Mor, Jordi Cipriano
Urban energy vulnerability index assessment at building level
Introduction
Urban energy vulnerability index assessment at building level
Energy vulnerability is a subjective combination of risk factors
in homes that can lead to energy poverty:
- Technological: Typology of the HVAC systems, domestic appliances, and their usage
- Social: Population and households characteristics, health conditions
- Economical: Household prices, salary indexes, energy costs, CPI
- Physical: Building envelope characteristics, weather conditions, air conditions
What is energy vulnerability?
/ Introduction
/ Introduction
How to assess the vulnerability of citizens?
-
Through the implementation of a methodology that integrates:
- Energy thermal building-stock modelling
- Weather model to upscale mesoscale resolution to microlocal
- Interdisciplinary and highly heterogeneous data sources
- Harmonisation of the datasets in a common database framework
Urban energy vulnerability index assessment at building level
/ Introduction
What is CLIMATE READY BARCELONA?
- ICLEI Action Fund 2.0 project (Budget: 1,000,000 €)
- Support citizens and public authorities to anticipate and adapt to climate-change effects and the energy crisis
- Implemented from July 2023 to June 2025
- Partners: Ecoserveis, ABD, BitGenoma, CIMNE, Barcelona City Council
Urban energy vulnerability index assessment at building level
Urban energy vulnerability index assessment at building level
Data
Urban energy vulnerability index assessment at building level
- Cadaster
- Energy Performance Certificate
- Electricity and Gas Consumption
- Buildings thermal demand and Technical Inspections
- Building-aggregated vulnerability surveys
- Weather data
- Socio-economic indicators
- Climate Shelters
- Postal code / Census tracts / Districts / Municipalities / Neighbourhood administrative layers
- Normalised Difference Vegetation Index (NDVI)
- Tourism-related establishments
- Mortality and morbidity due to extreme heat events
Datasets considered
/ Data
Urban energy vulnerability index assessment at building level
Modelling
Urban energy vulnerability index assessment at building level
Buildings Thermal Energy model
Weather upscaling resolution model
Graph Neural Network
Multifaceted Models: Diverse Objectives
/ Modelling
Urban energy vulnerability index assessment at building level
Buildings Thermal Energy model
Weather upscaling resolution model
Graph Neural Network
Multifaceted Models: Diverse Objectives
/ Modelling
Climate Vulnerability Index (CVI)
Urban energy vulnerability index assessment at building level
Weather upscaling resolution model
/ Modelling
Data communication and visualisation
Urban energy vulnerability index assessment at building level
Urban energy vulnerability index assessment at building level
User interface
/ Data communication and visualisation
Urban energy vulnerability index assessment at building level
User interface
/ Data communication and visualisation
Urban energy vulnerability index assessment at building level
User interface
/ Data communication and visualisation
Urban energy vulnerability index assessment at building level
User interface
/ Data communication and visualisation
Thanks for your attention
Arnau Comas
email: acomas@cimne.upc.edu
Enhancing Climate Resilience Through Urban Microscale Weather Data Analysis
By CIMNE BEE Group
Enhancing Climate Resilience Through Urban Microscale Weather Data Analysis
Urban energy vulnerability index assessment at the building level is crucial for understanding and addressing climate challenges in cities, particularly in Barcelona. This study, conducted as part of the Climate Ready Barcelona project (funded by ICLEI and Google), focuses on integrating diverse data sources specific to Barcelona, harmonizing them to an ontology framework, and utilizing Graph Neural Networks (GNNs) for data modeling. Data ingestors collect heterogeneous datasets, including cadaster, weather, energy consumption, simulated energy demand, vulnerability surveys, and socio-economic data, which are then harmonized to a standardized ontology, facilitating interoperability and consistency. GNNs are employed to impute gaps and detect anomalies in the data, producing a comprehensive dataset for vulnerability index computation. Key Performance Indicators (KPIs) such as energy consumption, building age, and socio-economic status will assess vulnerability in Barcelona. The results will be accessible via a user interface catering to various roles (citizens, urban planners, administrators), fostering informed decision-making and sustainable urban development in Barcelona.
