Abstract
Energy consumption in the building sector is about 40% of total energy consumed globally and is trending upwards, along with its contribution to greenhouse gas (GHG) emissions. Given the adverse impacts of GHG emissions, it’s crucial to integrate energy efficiency into building designs. The most significant opportunities for enhancing energy performance are present during the initial phases of building design, which are less impacted by other design constraints. Various tools exist for simulating different design options, providing feedback in terms of energy consumption and comfort parameters. These simulation outputs must then be analyzed to derive design solutions. This paper presents an innovative approach that utilizes user input parameters, processes them through cloud computing, and outputs easily understandable strategies for energy-efficient building design. The methodology employs Asynchronous Distributed Task Queues (DTQ)-a scalable and reliable alternative to conventional speedup techniques-for conducting parametric energy simulations in the cloud. The goal of this approach is to assist design teams in identifying, visualizing, and prioritizing energy-saving design strategies from a range of possible solutions for each project.
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Acknowledgments
The U.S. Department of Energy (DOE) and the Department of Science and Technology (DST), Government of India (GOI) provided joint funding for work under the U.S.-India Partnership to Advance Clean Energy Research (PACE-R) program’s “U.S.-India Joint Centre for Building Energy Research and Development” (CBERD) project. The authors would also like to acknowledge Amazon Web Services (AWS) for supporting this work through the AWS cloud credits research program and the Indorama Ventures Center for Clean Energy for their support in the ongoing development.
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Bhatia, A., Dontu, S., Garg, V., Haves, P., Singh, R. (2024). Early Stage Design Methodology for Energy Efficiency in Buildings Using Asynchronous Distributed Task Queues Framework. In: Jørgensen, B.N., da Silva, L.C.P., Ma, Z. (eds) Energy Informatics. EI.A 2023. Lecture Notes in Computer Science, vol 14467. Springer, Cham. https://doi.org/10.1007/978-3-031-48649-4_7
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