A data-driven framework for modelling community energy demand

Sandhya Patidar*, David P. Jenkins, Andrew Peacock, Kumar Biswajit Debnath

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

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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.
Original languageEnglish
Publication statusPublished - 5 Sept 2023
Event18th International IBPSA Conference and Exhibition: Building Simulation 2023 - Shanghai, China
Duration: 4 Sept 20236 Sept 2023


Conference18th International IBPSA Conference and Exhibition
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