This paper investigates three stochastic modelling procedures for generating N (user specified) synthetic annual electricity demand profiles at one-minute resolution. The paper reviews previous work in the application of Hidden-Markov modelling (HMM) for synthesizing highly stochastic time-series of domestic electricity demand through a sophisticated framework coalescing 480 distinct HMM. The efficiency of a proposed approach for integrating a time-series deseasonalizing technique with a single HMM has been studied in parallel with a compatible stochastic modeling framework of a time-series deseasonalized ARIMA model. Various statistical measures/characteristics of the real and synthetic profiles have been compared for all the three stochastic modelling procedures to identify the most efficient and practically suitable medium for generating synthetic electricity time-series at a fine temporal resolution. Results have been shown for both the individual buildings and the composite (aggregated) profiles of many buildings.
- Synthetic time-series
- Hidden Markov model
- ARIMA model
- TIme Series Decomposition
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- School of Energy, Geoscience, Infrastructure and Society - Associate Professor
- School of Energy, Geoscience, Infrastructure and Society, Institute for Infrastructure & Environment - Associate Professor
Person: Academic (Research & Teaching)