Market-Information-Aware Gated-LoRA of Foundation Models for Transferable Day-Ahead Electricity Price Forecasting
Bridging the data-scarce energy market gap with transferable foundation models and state-dependent adapters.
Electricity price forecasting is crucial for market participants but remains difficult because prices are volatile, market-specific, and closely tied to anticipated system conditions. Existing supervised methods depend largely on market-specific historical data, limiting their us...
ArXiv CS.LG
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This research bridges the data gap in electricity markets by leveraging foundation models that adapt via gated LoRA to external variables. It demonstrates how parameter-efficient fine-tuning can effectively navigate the high volatility and unique constraints of energy trading without requiring extensive local historical data.
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