Year: 2025
Type: preprint
Abstract: Electricity demand and generation have become increasingly unpredictable with the growing share of variable renewable energy sources in the power system. Forecasting electricity supply by fuel mix is crucial for market operation, ensuring grid stability, optimizing costs, integrating renewable energy sources, and supporting sustainable energy planning. We introduce two statistical methods, centering on forecast reconciliation and compositional data analysis, to forecast short-term electricity supply by different types of fuel mix. Using data for five electricity markets in Australia, we study the forecast accuracy of these techniques. The bottom-up hierarchical forecasting method consistently outperforms the other approaches. Moreover, fuel mix forecasting is most accurate in power systems with a higher share of stable fossil fuel generation.
Source: arXiv (Cornell University)
Authors: Shang, Han Lin, Han, Lin, Trück, Stefan
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7102596630
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