India UK

Integrated Urban Model for Built Environment Energy Research (iNUMBER)

Improving Data Granularity

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Improving Data Granularity
Leads: Rajan Rawal (CEPT) and Kathryn Janda (UCL)

Energy meters (Left image) and Environment sensors (Right image) installed in dwelling units to capture energy and indoor environment datasets.

WP3 enriches the “data poor” contexts through three different modes of high-resolution data capture non-intrusive, intensive, and extensive to provide a ‘real world’ context for the urban model’s data analytics. Partners include Zero Energy Developed (ZED) homes and Pilio Group.

Non-intrusive methods of data capture included deploying LiDAR and GIS technology to generate data pertaining to building characteristics over large area. The aim was to develop detailed model which helps to predict future energy demand reduction in the case of various technology deployment, city design alternatives and integration of rooftop solar PV potential. This WP is also going to identify opportunities which will help to operate buildings in mixed mode comfort operation. After pertaining appropriate agreements and licensing, using unmanned aerial vehicles (UAVs) with visible photography and infrared thermography information have been gathered on 3D form of the built environment, building materials, fenestration patterns and heat flows. These data allow iNUMBER to model and anticipate the benefits of changes to future stock in terms of window placement and shading, natural ventilation opportunities, and solar access, as well as provide opportunities for retrofitting the existing stock with photovoltaic and/or exterior shading.
This also focused on intensive data collection within residential buildings with an objective to capture thermal comfort conditions and energy consumption associated with the use of home appliances and occupant behaviour. This project aimed to work in 50-60 multi-family, multi-storey apartmentsas well as individual dwelling units covering the different economic cross sections of society in Ahmedabad city of India. It gathered detailed information using best available techniques.
Extensive data collection used lower tech participatory tools and methods for gathering energy and water consumption data for homes and municipal energy services. iNUMBER work in this area was aimed to build on two EPSRC projects on time of use and energy management: (1) Dr Grunewald’s Measuring and Evaluating Time and Energy use Relationships (METER, EP/M024652/1) which is based on SEEMs, and (2) a project focused on energy strategies in the retail sector (WICKED, EP/L024357/1). Smart Energy and Environmental Monitors (SEEMs) are based on Android smartphones + a peripheral measurement device. SEEMs were developed and patented through WICKED and an EPSRC Impact Acceleration Account held by Dr Layberry. However, the conventional monitors were used and SEEMs could not be used in extensive data collection process. Conventional monitors allowed (1) gathering of granular data about technologies, premises, and electricity use in dwelling units and (2) exploration of opportunities for demand-response and demand side management. As part of this work package, these conventional monitors were installed in 67 dwelling units of different building typologies over the course of the project.

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