Optimising Battery Energy Storage System Trading via Energy Market Operator Price Forecast
Machine learning return prediction, studied by Fabre in 2025. The abstract mentions profits but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“This work addresses a key research question: Can the accuracy of the Australian Energy Market Operator (AEMO) energy price forecasts be systematically leveraged to develop a reliable and profitable battery energy storage system trading algorithm?”
StrategyMachine learning
ClaimProfit
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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