A data-driven framework for modelling community energy demand

Sandhya Patidar; David P. Jenkins; Andrew Peacock; Kumar Biswajit Debnath

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 conference › Paper › peer-review

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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.

Original language	English
Publication status	Published - 5 Sept 2023
Event	18th International IBPSA Conference and Exhibition: Building Simulation 2023 - Shanghai, China Duration: 4 Sept 2023 → 6 Sept 2023 https://bs2023.org/

Conference

Conference	18th International IBPSA Conference and Exhibition
Country/Territory	China
City	Shanghai
Period	4/09/23 → 6/09/23
Internet address	https://bs2023.org/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

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title = "A data-driven framework for modelling community energy demand",

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.",

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AU - Peacock, Andrew

AU - Debnath, Kumar Biswajit

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AB - 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.

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T2 - 18th International IBPSA Conference and Exhibition

Y2 - 4 September 2023 through 6 September 2023

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