As cities are expanding and small towns get upgraded to a city, energy demand also grows significantly. While the purview of existing building codes for energy efficiency (ECBC 2017 and ENS 2018) are focused on building level strategies, for a transition towards net zero targets at the scale of cities, tools like UEMs hold an immense potential.
The webinar brought on board Prof. Rajan Rawal, Executive Director CARBSE who explained in depth the concept of urban energy models and the work undertaken for the city of Ahmedabad.
Urban energy models (UEM) are critical data driven tools that support energy efficient climate resilient city planning and its operations. They also play a distinct role in accessing decentralized renewable energy generation potential within a city which in turn helps design and operate net zero neighborhoods.
Energy is also consumed by a building beyond its plot boundaries during its operation. Services such as pumping of water and wastewater at stations, transportation, and sorting of solid waste at distant facilities are examples of energy consumption at city level. The cost of these services is borne by municipal corporation or by other government bodies. And are not accounted for in building EPI (Energy Performance Index – a metric indicating energy use in a building), a performance parameter considered by energy codes and green building rating systems.
Over a period of 4-5 months, a bottom-up approach was adopted for analyzing the city of Ahmedabad. Prof. Rajan explained that a bottom-up approach where a detailed representation of each system and their constituents takes part in an aggregated whole building system level is more suitable for urban and regional analysis. The area of application of previous studies were also discussed.
The extent of the geometry data gathered and modeled depended upon the level of details. The idea was to create a model fit for purpose. A UAV (Unmanned Aerial Vehicle) was flown over the city of Ahmedabad to capture details of building morphology precisely up to 100mm. The data captured was filtered prior to constructing a model. Machine learning and artificial intelligence assisted in recognizing elements based on certain character.
UEM relies on the power of computation and information technology. While information like the occupancy, equipment & lighting schedules and construction templates are defined for the entire region in the model, image recognition and thermographic together aid in deducing the window to wall ratio, construction type, shading practices etc.
While a city experiences organic growth, its growth may not follow the scheme of engineering services. The command area of different services such as water pumping, wastewater conveyance may not coincide/overlap with its building clusters. The relationship between the two can be comprehended by mapping the clusters and service command areas to a high accuracy and determining the relationship between each unit of the service and electricity consumption. It is then possible to determine the actual energy consumption of a particular building in a larger context.
This type of detailed analysis can be scaled up and made financially more efficient in the future. UEMs may guide policymakers in taking informed decisions particular to the distinct networks and working of each city. The actual implications and realizing the cumulative impact of implementation of existing energy efficiency codes on carbon emission, technology adoption, skill uptake, upcoming developments and processing units at a city level can be done by analyzing comprehensive outputs from UEMs.
This webinar was conducted on 9th April 2021.