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EARLY DESIGN METHODOLOGY FOR ENERGY EFFICIENT BUILDING DESIGNAuthor: Aviruch Bhatia Date: 2019-07-17 Report no: IIIT/TH/2019/98 Advisor:Vishal Garg AbstractEnergy consumed in the building sector accounts for 20.1% of the total delivered energy consumed worldwide. In 2016, the residential and commercial sectors in the US accounted for around 40% of the total US energy consumption, which largely contributes to green house gas (GHG) emissions. Immediate action in the building sector is essential to avoid hazardous climate change and also to incorporate energy efficiency in the building design. The early design stage offers more opportunities for use of energy simulation in achieving energy efficiency because there are fewer constraints from other design decisions. Although there are a large number of building simulation tools available, these tools are mostly limited to the design development stage and not the early design stage. In the early design stage, there are a large number of variables involved, and it is difficult for design teams to consider all the solutions and take decisions because this might lead to missing some parameter combinations. In addition, in the early design stage, there are very few users of building energy simulation tools because: • Performing energy simulation requires sufficient knowledge of building physics and expertise • Simulating a large number of combinations takes significant time and computation power • After simulating a large number of combinations, extracting relevant information from output data can be tedious and difficult to understand To fill this gap, an early design methodology has been developed for energy efficient building design. The methodology includes: 1. Getting inputs from the user in a simple user interface 2. Generating input files for the simulations 3. Performing energy simulations efficiently by using parallel simulations and/or using machine learning techniques to speedup simulations 4. Identifying clusters in output data to communicate the strategies to users in the form of humanly describable rules. The contributions of this thesis are as follows: (i) Machine learning regression techniques to speed up the parametric simulations. There were two case studies performed for two different cities in which the use of two algorithms (kNN and SVR) were shown to increase speed of parametric energy simulations by approximately 40 times. (ii) Axis Aligned Hyper Rectangle (AAHR) clustering method for building energy simulation data was used to produce humanly describable or easy to understand rules for design strategies. An energy model was developed and simulated for five cities in different climates and findings from these are discussed in the thesis. (iii) Development of a tool called eDOT to demonstrate the working of early design methodology. eDOT considers five variable parameters, which are orientation, aspect ratio, window to wall ratio (WWR), overhang profile angle, and glass type. Once the user enters these parameters, backend simulations are performed in parallel on the cloud. The user is provided with interactive visualisation of output data. An algorithm is applied to find strategies in output data in humanly understandable rules. To use this tool, the user does not need not to be an expert at using any energy simulation tools. (iv) Development of the Surrogate City Finder (SCF) tool to find similar weather data based on location, latitude range, altitude range, temperature range and distance range. (v) An approach was developed using EnergyPlus simulations to calculate the equivalent SHGC for various fixed/dynamic shading configurations. For the fenestration, the prescriptive method provides maximum allowable U-value and Solar Heat Gain Coefficient (SHGC). Some modifications are allowed to SHGC if fixed horizontal overhangs and/or vertical fins are installed on the fenestration. The modified SHGC with shades is called the equivalent SHGC for code compliance requirements. In this approach, equivalent SHGC is calculated using energy simulations for a reference case (with fixed/dynamic shades) and other parametric cases with varying SHGC (without shades). Full thesis: pdf Centre for IT in Building Science |
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