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Urban Energy Modeling

Executive Summary:
The project is carried out in collaboration with the Massachusetts Institute of Technology. At Masdar Institute, the team comprises Prashanth Marpu, Zeyar Aung, Chi-Kin Chau, and Afshin Afshari. At MIT, the team comprises Leslie Norford, Steven Leeb, and Christoph Reinhart. This project will develop an appropriate framework for monitoring, modeling and ultimately manipulating the urban microclimate for efficient adaptation of future urban infrastructure. The work combines aspects of assimilation of high-resolution data from remote and terrestrial wide-area sensor networks and advanced modeling of the intricate relationship between urban microclimate and urban form, expressed through such phenomena such as the urban heat island effect, climate change, local wind patterns and solar shading.

The project consists of five elements:

  • Explore and develop a combination of wireless and wired sensor-network and data-processing architectures inside and outside buildings to support microclimate modeling through assimilation of recorded and analyzed data while minimizing the cost of sensors, their deployment and necessary communication and power infrastructures.
  • Significantly extend the capability of a recently developed (at MIT) urban microclimate model and the modeling work of others by introducing vertical resolution of street canyon walls with accurate radiant exchange models such that the steady-state temperature gradients of vertical surfaces can be modeled with greater fidelity. Concurrently, MI will develop and validate an existing urban energy model incorporating detailed representations of typical Abu Dhabi buildings as well as a dynamic characterization of the urban heat island. The combination of higher vertical resolution of surface heat balance and storage, together with the introduction of a vehicle energy-use model, will result in improved air temperature predictions. While the MIT model will focus on the outdoor microclimatic phenomena (with an approximate lumped parameter representation of the indoor thermal exchanges), the MI model is geared towards efficient and accurate estimation of building cooling load.
  • Validate/calibrate the new models using air- and mean-radiant-temperatures measured at 50 16-channel stations in Abu Dhabi (MI and MIT models) as well as substation-level electricity load measurements recorded by the utility SCADA system (MI model). The experimental design and deployment of stations will be interactive, using models to identify street canyon locations/characteristics that are likely to produce the strongest responses to urban-heating forcing functions.
  • Test the sensitivity of model results (temperatures, building cooling energy, outdoor thermal comfort) to model specification of urban morphology, land surface type, building exterior features (geometry and albedo), and anthropogenic heat sources (building heat rejection; traffic). The models can be run with three levels of inputs: largely assumed parameters and minimal forcing form site-specific data; additional data at hand; and very detailed data from an extensive sensor network and remote sources. At each level, there are uncertainties associated with assumed or measured inputs. We will test the full range of application modes to understand the impact of model specification uncertainty and provide guidance to the user community.
  • Embed the validated microclimatic model in a recently developed urban-scale environmental modeling platform developed at MIT called umi ( to propose urban policy measures that can temper urban heat in order to improve outdoor occupant comfort, reduce building energy use. Concurrently, embed the validated MI urban energy model in a decision support platform to choose optimal city-wide building retrofit interventions to attenuate

Scope of the work:
The objective of the proposed work is to develop an urban sensing and modeling system that will support two important activities: the preparation and assessment of policy and regulatory measures concerning the urban thermal environment; and the simulation-aided design of urban neighborhoods to account for the coupled interactions of buildings and their environment.

The scope of work starts with the development of forward (physics-based, as distinct from inverse data-driven) models to predict thermal conditions in the urban environment given meteorological forcing variables (direct and diffuse radiation, far-field temperature and wind), anthropogenic sources (heat produced by vehicle use and building Heating, Ventilation and Air conditioning (HVAC), and the physical details of a given urban landscape. A data assimilation model will be developed that can use remotely sensed radiosity (emissive and reflective power) in various bands to estimate thermal conditions (even in proximity of surfaces not visible to satellite-borne sensors) when forward model inputs are not available or are of uncertain quality. Data from novel building energy monitors will also be used to estimate building-sourced anthropogenic heat inputs to the model. We will use these tools to assess policy actions (building codes, energy efficiency retrofits, mix of transportation modalities and efficiencies) and new urban planning mitigation strategies pertaining to streets, parks, building form, and other urban features that interact with microclimate, orientation and density, mixed use, pedestrian and other access.

The total cost of the project at Masdar Institute is ~1.44 million USD for 3 years. This cost includes both direct and indirect cost.