面向可迁移日前电价预测的、融合市场信息的基础门控低秩适配（Gated-LoRA）模型
Market-Information-Aware Gated-LoRA of Foundation Models for Transferable Day-Ahead Electricity Price Forecasting
AI总结：
本文提出融合市场信息的Gated-LoRA框架，将Chronos-2模型迁移至日前电价预测，经中国四省现货市场验证，可显著降低预测误差，为数据稀缺电力市场提供迁移方案。
AI中文摘要：
电价预测对市场参与者至关重要，但由于电价波动、具有市场特异性且与预期系统条件密切相关，预测难度较大。现有监督方法很大程度上依赖特定市场的历史数据，限制了其在新建或数据稀缺市场中的应用。本文提出一种融合市场信息的适配框架，将Chronos-2时间序列基础模型迁移至日前电价预测任务。该框架首先构建多源市场信息（MSMI）接口，将7天价格上下文与预清算供需、备用容量、机组检修、发电机容量及联络线变量对齐；随后训练源域门控低秩适配（LoRA），仅更新约1%的模型参数，无需目标市场标签。门控机制根据备用紧张度和运行状态信号缩放冻结的源域适配器。采用留一市场法协议评估跨市场可迁移性，在四个中国省级日前现货市场开展的实验显示，与融合市场信息的零样本Chronos-2相比，所提框架将平均平均绝对误差（MAE）降低6.24%、平均均方根误差（RMSE）降低7.99%；与普通源域LoRA相比，分别降低3.05%、3.52%。实验还表明，该增益无法通过学习全局标量或随机门初始化实现，且相较于源域LoRA的额外提升有限。这些结果说明，市场结构化输入和状态依赖型门控LoRA可为数据稀缺的电力市场提供实用的迁移路径。
英文摘要：
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.
