Market-Information-Aware Gated-LoRA of Foundation Models for Transferable Day-Ahead Electricity Price Forecasting

• URL: http://arxiv.org/abs/2608.11359v1
• Date: Tue, 11 Aug 2026 19:11:32 GMT
• Title: Market-Information-Aware Gated-LoRA of Foundation Models for Transferable Day-Ahead Electricity Price Forecasting
• Abstract summary: This paper proposes a market-information-aware adaptation framework that transfers the Chronos-2 time-series foundation model to day-ahead electricity price forecasting.<n> Experiments show that the proposed framework reduces the average MAE/RMSE by $6.24%/7.99%$ relative to market-information-aware zero-shot Chronos-2 and by $3.05%/3.52%$ relative to vanilla Source-LoRA.
• Score: 6.569678181953392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 use in newly established or data-scarce markets. This paper proposes a market-information-aware adaptation framework that transfers the Chronos-2 time-series foundation model to day-ahead electricity price forecasting. It first constructs a multi-source market information (MSMI) interface aligning 7-day price context with pre-clearing supply--demand, reserve, maintenance, generator-capacity, and intertie variables, and then trains a source-domain gated low-rank adapter (LoRA), updating about $1\%$ of model parameters without target-market labels. The gate scales the frozen source adapter according to reserve-tightness and operating-state signals. A leave-one-market-out protocol is adopted for evaluating cross-market transferability. Experiments on four Chinese provincial day-ahead spot markets show that the proposed framework reduces the average MAE/RMSE by $6.24\%/7.99\%$ relative to market-information-aware zero-shot Chronos-2 and by $3.05\%/3.52\%$ relative to vanilla Source-LoRA. Experiments show that the gain is not reproduced by a learned global scalar or by random gate initialization, while the additional improvement over Source-LoRA is limited. These results suggest that market-structured inputs and state-dependent gated LoRA can provide a practical transfer path for data-scarce electricity markets.

Related papers

• A Few-Shot LLM Framework for Extreme Day Classification in Electricity Markets [1.0730888578919362]
  This paper proposes a few-shot classification framework based on Large Language Models (LLMs) to predict whether the next day will have spikes in real-time electricity prices.<n>Using historical data from the Texas electricity market, we demonstrate that this few-shot approach achieves performance comparable to supervised machine learning models.
  arXiv  Detail & Related papers  (2026-02-17T20:54:44Z)
• Forecasting Future Language: Context Design for Mention Markets [81.25011140991566]
  We study how input context should be designed to support accurate prediction in mention markets.<n>We find three insights: (1) richer context consistently improves forecasting performance; (2) market-conditioned prompting (MCP) treats the market probability as a prior and updates it using textual evidence, yields better-calibrated forecasts; and (3) a mixture of the market probability and MCP (MixMCP) outperforms the market baseline.
  arXiv  Detail & Related papers  (2026-02-04T12:43:31Z)
• Leveraging Asynchronous Cross-border Market Data for Improved Day-Ahead Electricity Price Forecasting in European Markets [0.0]
  We show significant improvements of 22% and 9% in forecast accuracy in the Belgian (BE) and Swedish bidding zones.<n>This improvement holds for both general as well as extreme market conditions.<n>Our analysis also yields further important insights: frequent model recalibration is necessary for maximum accuracy but comes at substantial additional computational costs.
  arXiv  Detail & Related papers  (2025-07-17T15:59:00Z)
• ML-Based Bidding Price Prediction for Pay-As-Bid Ancillary Services Markets: A Use Case in the German Control Reserve Market [45.62331048595689]
  This paper presents a methodology for forecasting bidding prices in pay-as-bid ancillary service markets, focusing on the German control reserve market.<n>We evaluate various machine learning models, including Support Vector Regression, Decision Trees, and k-Nearest Neighbors, and compare their performance against benchmark models.<n>Our analysis demonstrates that the proposed approach improves potential revenues by 27.43 % to 37.31 % compared to baseline models.
  arXiv  Detail & Related papers  (2025-03-21T15:21:43Z)
• Probabilistic Forecasting of Real-Time Electricity Market Signals via Interpretable Generative AI [41.99446024585741]
  We present WIAE-GPF, a Weak Innovation AutoEncoder-based Generative Probabilistic Forecasting architecture. A novel learning algorithm with structural convergence guarantees is proposed, ensuring that the generated forecast samples match the ground truth conditional probability distribution.
  arXiv  Detail & Related papers  (2024-03-09T00:41:30Z)
• HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE [113.47287249524008]
  It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting. We propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the relationship between the market situation and stock-wise latent factors. Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
  arXiv  Detail & Related papers  (2023-06-05T12:58:13Z)
• Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution [0.0]
  We propose a modelling strategy for the location, shape and scale parameters of the return distribution in intraday markets. We consider wind and solar forecasts and their intraday updates, outages, price information and a novel measure for the shape of the merit-order. We find that the volatility is driven by the merit-order regime, the time to delivery and the closure of cross-border order books.
  arXiv  Detail & Related papers  (2022-11-23T15:08:50Z)
• OSOUM Framework for Trading Data Research [79.0383470835073]
  We supply, to the best of our knowledge, the first open source simulation platform, Open SOUrce Market Simulator (OSOUM) to analyze trading markets and specifically data markets. We describe and implement a specific data market model, consisting of two types of agents: sellers who own various datasets available for acquisition, and buyers searching for relevant and beneficial datasets for purchase. Although commercial frameworks, intended for handling data markets, already exist, we provide a free and extensive end-to-end research tool for simulating possible behavior for both buyers and sellers participating in (data) markets.
  arXiv  Detail & Related papers  (2021-02-18T09:20:26Z)
• Learning the Gap in the Day-Ahead and Real-Time Locational Marginal Prices in the Electricity Market [0.0]
  Machine learning algorithms and deep neural networks are used to predict the values of the price gap between day-ahead and real-time electricity markets. The proposed methods are evaluated and neural networks showed promising results in predicting the exact values of the gap.
  arXiv  Detail & Related papers  (2020-12-23T16:49:24Z)
• Transfer Learning for Electricity Price Forecasting [0.0]
  We propose to use transfer learning as a tool for utilizing information from other electricity price markets for forecasting. Our experiments on five different day-ahead markets indicate that transfer learning improves the performance of electricity price forecasting in a statistically significant manner.
  arXiv  Detail & Related papers  (2020-07-05T17:24:36Z)
• A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding [69.37299910149981]
  A key component for the successful renewable energy sources integration is the usage of energy storage. We propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market. An distributed version of the fitted Q algorithm is chosen for solving this problem due to its sample efficiency. Results indicate that the agent converges to a policy that achieves in average higher total revenues than the benchmark strategy.
  arXiv  Detail & Related papers  (2020-04-13T13:50:13Z)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States [71.54651874063865]
  Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks. In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
  arXiv  Detail & Related papers  (2020-02-09T08:10:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.