Forecasting Australian Electricity Generation by Fuel Mix

TL;DR

This paper presents statistical methods for short-term forecasting of electricity supply by fuel type in Australia, emphasizing the importance of accurate predictions for grid stability and renewable integration.

Contribution

It introduces two novel statistical techniques based on forecast reconciliation and compositional data analysis for fuel mix prediction.

Findings

Hierarchical forecasting outperforms other methods.

Forecast accuracy improves with higher fossil fuel share.

Methods are validated on five Australian electricity markets.

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.