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A data-driven framework for modelling community energy demand

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.

Publication metadata

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)


DOI: 10.26868/25222708.2023.1673

Library holdings: Search Newcastle University Library for this item

Series Title: Building Simulation Conference Proceedings

ISBN: 9781775052036