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arXiv Machine Learning By Btissame El Mahtout, Florian Ziel

Electricity Price Forecasting: Bridging Linear Models, Neural Networks and Online Learning

Read the original on arXiv Machine Learning →

The paper presents a hybrid neural architecture that blends linear and nonlinear feed‑forward networks for day‑ahead electricity price forecasting. It introduces a partial online learning strategy with warm‑starting and stage‑specific hyperparameters to cut computational time, and employs Bernstein Online Aggregation to combine forecasts. Experiments on six years of major European markets show the method reduces RMSE by 11‑12% and MAE by 14‑17% compared to state‑of‑the‑art benchmarks while lowering computational cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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