A data-driven framework for modelling community energy demand

Patidar, S; Jenkins, DP; Peacock, A; Debnath, KB

doi:10.26868/25222708.2023.1673

A data-driven framework for modelling community energy demand

Lookup NU author(s): Professor David Jenkins, Dr Kumar Biswajit Debnath ORCiD

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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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Abstract

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.