Report
Using multivariate time series to estimate location and climate change effects on historical temperatures employed in future electricity demand simulation
Murdoch University Research Report, Mathematics and Statistics
2016
Abstract
Long-term historical daily temperatures are used in electricity forecasting to simulate the probability distribution of future demand but can be affected by changes in recording site and climate. This paper presents a method of adjusting for the effect of these changes on daily maximum and minimum temperatures. The adjustment technique accommodates the autocorrelated and bivariate nature of the temperature data which has not previously been employed. The data is from Perth, Western Australia, the main electricity demand centre for the South-West (of Western Australia) Interconnected System. The statistical modelling involves a multivariate extension of the univariate time series “interleaving method”, which allows fully efficient simultaneous estimation of the parameters of replicated VARMA processes. Temperatures at the most recent weather recording location in Perth are shown to be significantly lower compared to previous sites. There is also evidence of long-term heating due to climate change especially for minimum temperatures.
Details
- Title
- Using multivariate time series to estimate location and climate change effects on historical temperatures employed in future electricity demand simulation
- Authors/Creators
- R.S. Bowden (Author/Creator)B.R. Clarke (Author/Creator)
- Publisher
- Murdoch University Research Report, Mathematics and Statistics
- Identifiers
- 991005541331507891
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Report
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