WP1 – Urban analytics
Urban systems are the engines of consumption and economic growth. Sustainable development thus depends on a better understanding on how the use of natural resources correlates with urban economic activities. The main objectives of WP1 are to provide quantitative assessments of these correlations by developing and applying a new suite of models concerning energy consumption, material use and mobility needs. The models will address the consumption of energy, the use of materials, the need for mobility and the cross implications of the material-energy nexus through the embodied energy of materials. The final goal is to identify and assess the merit of candidate sustainable urban solutions.
This WP is organized in 5 tasks, related to energy, urban metabolism, mobility and buildings retrofit:
The urban energy simulations will be based on the MIT urban model interface (UMI) program. UMI is a next generation urban performance simulation tool that can efficiently model the environmental performance of multiple buildings, approximate microclimatic effects, and consider multiple sustainable performance metrics. UMI specifically supports outputs of operational energy use based on the US DOE EnergyPlus whole building simulation engine (link)
embodied energy use and life cycle performance over multiple years; annual daylight availability and basic non-motorized travel behavior predictions. We will focus on improving the integration of these tools and test various energy supply side options, as a tool to design sustainable options in the urban space. We will validate and improve the model in the Lisbon testbed. Results from UMI will be shared and converted into data formats to be used in other tasks and WPs.
This task focuses on developing an urban metabolism simulator (UMS), based on econometric studies that characterize key socioeconomic and infrastructural drivers for resource consumption. The key innovation consists on the characterization of a suite of candidate urban interventions that will be parameterized in terms of cost and potential resource savings in energy and materials use. Examples of these interventions include local food production and resource efficiency interventions (e.g. rainwater harvesting, rooftop greenhouses, green facades, organic waste local valorization, etc.). This will be coupled with an optimization algorithm to provide the best solutions for each set of constraints defined by policy makers. This UMS will also be used to quantify sustainability indicators that may enable neighborhood competitions and to support the benchmarking of different urban systems in terms of resource consumption, contributing to the design of urban systems certification schemes based on resources other than energy (already in place).
This task focuses on improving a mobility model that features a systemic view to assess the flows of people and goods, in order to provide the basic inputs for policy and planning. It will correlate building occupancy data with the mobility requirements of their occupants and analyze existing travel diaries and data gathered from passive and onboard sensing to develop activity chains and their associated transportation needs. The model will be structured in four main blocks: the modeling of the activity and mobility patterns of households; the development of the real estate market; the transportation system design (infrastructure) and regulation; and the public transport operation (specification of the attributes of the services provided). We will relate our observations of individual travel demand and mode sharing with new metrics of the “walkability score” related to the design of the urban landscape, the efficiency of the public transport, and vehicle ownership rate. The households analyzed will be the potential recipients of the efficient mobility alternatives proposed in WP5. The proposed transportation solutions will be displayed in the interactive web-platform of WP6.
Building Energy Savings Potential
This task is focused in up-scaling the energy model of Task 1 to make energy saving predictions at urban scale resulting from potential buildings retrofit. A vital contribution to improve energy efficiency in cities is a computational method that may estimate energy codes of all its buildings solely by benchmarking energy consumption. This builds up on a recent development of a computational platform to do physical simulations and large-scale data analysis on thousands of buildings and their energy bills. These results constitute the building blocks of an urban simulator (EnergySaver) that analyzes the empirical losses of each building, to calculate a surrogate function of a reduced set of physical variables and allows us to tackle the physical domain in which each analyzed building belongs. The goal is to refine and test this simulator by validating its predictions with field measurements in the Lisbon testbed. A key development is to explore complementary scenarios with better understanding of the impact of climate and building design as the outcomes from UMI (task 1) in the resulting energy consumption.
We will develop a 3D reproduction of the testbed neighborhood, where the results from the models can be projected and analyzed. This representation will be based on CityScope, an advanced decision support system for cities usable by expert and non-expert stakeholders developed at MIT, Media Lab (link). CityScope uses high-definition video projectors, advanced modeling and simulation, 3D projection mapping, and physical models to create a tangible, interactive, real-time data observatory and urban intervention simulator. This will allow for the simulation of urban interventions using models developed in the previous tasks, status and topology of the electrical grids and a specified type of information for both network operators.
Testbed component – this WP will contribute with detailed modeling of hundreds of buildings at the “Parque das Nações” (link), an area of 5,44 km2 and approximately 21,000 citizens. Information will be provided in ARCGIS, including building footprints, shape files and building age. Task 5 will provide a unique 3D model of the area exploring the MIT CityScope that will represent the dynamics of the results obtained both in energy or mobility dimensions, including the impact of the interventions suggested.