A new approach to fundamental electricity market modelling: Exploring market dynamics and speculator influence

Fundamental short-term electricity market models typically rely on expert-driven assumptions and manual, iterative calibration, and often neglect strategic bidding behaviours. To address these issues, we develop a bottom-up (fundamental) model that forecasts day-ahead outcomes from publicly available participant order data using regularised regression, machine learning, and neural network techniques. We introduce an explainability framework that decomposes forecast errors at participant, cohort, and aggregate levels, linking forecast performance to forecast trading behaviours. Compared with a benchmark top-down model, the bottom-up approach yields lower price forecast accuracy but demonstrates an ability to capture market dynamics. Where forecast dynamics diverge from observed outcomes, many misaligned cases are attributable to specific cohorts, particularly financial traders (speculators). Beyond forecasting, the framework offers complementary applications: the modelling approach can support calibration of traditional fundamental models and serve as a stand-alone forecaster in markets beyond day-ahead where order data are available, while the explainability component can apply to both bottom-up and hybrid modelling approaches. The study highlights the challenges inherent in bottom-up fundamental models, while showing how our approach provides new insights and practical tools to support their calibration and application.