A data driven system for associating climate with Energy demand modelling

Sandhya Patidar*, David P. Jenkins, Andrew Peacock

*Corresponding author for this work

Research output: Contribution to conferencePoster

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Abstract

The presentation will showcase a novel system of data science approaches for associating climatic information within the energy demand simulation and modelling. The modelling system integrates a time series decomposition process for extracting long term climatic and energy demand trends that can be associated statistically using a partial least square regression. The stochastic components are then subsequently modelled using a Hidden Markov model. The modelling system accounts for the extreme events using an extreme value distribution to simulate impact of extreme climatic impacts along with long term climate change impacts on the future energy demand protections. The modelling system is designed to account for climatic uncertainty in the projections to support decisions making, future infrastructure planning and policy development.
Original languageEnglish
Publication statusPublished - 26 Sept 2024
EventNext Generation Energy Climate Modelling 2024 - Department of Meteorology, Fully Online, Reading, United Kingdom
Duration: 26 Sept 202427 Sept 2024
https://research.reading.ac.uk/met-energy/next-generation-challenges-workshop/next-generation-energy-climate-modelling-2024/

Conference

ConferenceNext Generation Energy Climate Modelling 2024
Country/TerritoryUnited Kingdom
CityReading
Period26/09/2427/09/24
Internet address

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