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Lookup NU author(s): Professor David Jenkins, Dr Kumar Biswajit DebnathORCiD
This is the final published version of a conference proceedings (inc. abstract) that has been published in its final definitive form by International Building Performance Simulation Association (IBPSA), 2023.
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Data driven models that integrate advanced analytics involving statistical and machine learning algorithms are widely applied for simulating and predicting energy demand at the community level. These models are used to inform various energy efficiency measures, infrastructure development, planning and investment decision. The paper presents an innovative framework for simulating and projecting climate change impacts on the future dynamics of community energy demand. The modelling framework selectively couples some of the most advanced analytical approaches and its potential are demonstrated using a case study community “Auroville” located in India.
Author(s): Patidar S, Jenkins DP, Peacock A, Debnath KB
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Proceedings of Building Simulation 2023: 18th Conference of IBPSA
Year of Conference: 2023
Pages: 1239-1247
Online publication date: 06/09/2023
Acceptance date: 05/09/2023
Date deposited: 17/04/2024
ISSN: 2522-2708
Publisher: International Building Performance Simulation Association (IBPSA)
URL: https://doi.org/10.26868/25222708.2023.1673
DOI: 10.26868/25222708.2023.1673
Library holdings: Search Newcastle University Library for this item
Series Title: Building Simulation Conference Proceedings
ISBN: 9781775052036