What will the world of tomorrow look like? The world is changing rapidly, at a pace faster than ever witnessed at any other time in human history. By 2030, 5 billion people will live in Asia alone. City governments all over the world are realizing this alarming phenomenon and are preparing their cities to accommodate this ever increasing population. Taking note, the government of India launched a program to develop 100 SMART Cities in India in 2015, with Energy-Efficiency as a key feature. Thus the quest of this research is to develop a software workflow that will aid to develop an Energy Master Plan for one of the proposed SMART cities in India – New Delhi.
The developed tool helps to understand the spatiotemporal energy demand patterns at an urban scale. Based on the new generation of analytical “bottom-up” models called Urban Building Energy Models (UBEM), it allows to predict urban energy use in hourly time steps down to the individual building level & to simulate the combined impact of several energy efficiency measures in buildings, including the capability to simulate the effect of modified occupant behavior. The outcome of this research is a semi-automated tool called TRNZilla, which is developed as an extension for TRNLizard – a Grasshopper based interface for the dynamic energy simulation engine TRNSYS.
The framework begins the process of developing UBEM by utilizing urban geospatial datasets. Once the urban geometry and GIS datasets are imported in the Rhinoceros3d (Rhino) & Grasshopper environment, the TRNZilla algorithm automatically rationalizes them to suit the purpose of Building Energy Modelling (BEM). This is achieved by making informed simplifications to the urban geometry & datasets, by clustering them into archetypes. It also simplifies large building massing models through an abstraction process of creating a multi-zone network of single space ‘Typical Room’ models. This is a major step forward from the current state-of-the-art in UBEM framework which deploys single-zone models with adiabatic boundary conditions. It is the authors’ hope that this will enable to better simulate the energy demand in mixed use and / or mixed ventilation mode buildings, such as those found in developing countries like India. Further, these clusters or ‘archetype units’ are based on the variations in architectural programming, building envelope, ventilation mode and space conditioning systems. The TRNZilla algorithm also takes into account the different micro-climatic boundary conditions that may develop in a building due to the different façade orientations & urban contextual shading.
Thus, this research facilitates robust urban building energy modelling, with practically reasonable simulation times acceptable for professional work. This will enable city governments and urban planning authorities in developing an efficient Energy Master Plan to achieve their urban energy efficiency targets.
Mentors: Alejandra Cassis, Christian Frenzel, Christian Oberdorf, Elmira Reisi, Felix Thumm, Stefan Holst, Tommaso Bitossi
Mohammad Hamza – India
Hamza studied architecture at the Aligarh Muslim University and graduated with a Bachelor Degree with his thesis about the FIFA World Cup Stadium in Al-Khor, Qatar. He gathered work experience as intern architect at Hoxton & Urban in New Delhi.