Abstract
This paper presents a novel approach for developing a Demand Profile Synthesis Tool (DPSTool) involving the decomposition of electricity demand into subcomponents and selectively applying cutting-edge statistical modelling techniques for predicting (short/long-term) domestic energy demand patterns. The DPSTool will be underpinned by a new ‘climate module’ integrated within a previously developed high-tech HMM_GP model. The ‘climate module’ can simulate the impacts of key climatic variables on the electricity demand scenarios. The paper will demonstrate the key stages of statistical methodology development and validation procedure of ‘climate module’ using a residential case-study building selected from a community ‘Auroville’, located in India.
Original language | English |
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Number of pages | 8 |
Publication status | Published - 1 Sept 2021 |
Event | 17th IBPSA Conference 2021 - Bruges/Virtual, Bruges, Belgium Duration: 1 Sept 2021 → 3 Sept 2021 https://bs2021.org/ |
Conference
Conference | 17th IBPSA Conference 2021 |
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Country/Territory | Belgium |
City | Bruges |
Period | 1/09/21 → 3/09/21 |
Internet address |
Keywords
- climate change
- Energy Demand
- Forecasting
- Hidden Markov model
- STL_HMM_GP