A Numerical–Textual Dual-Channel Forecasting Framework for Short-Term Load Forecasting

Abstract

Short-term load forecasting underpins power-system dispatching, yet classical models degrade when historical samples are scarce. This paper proposes NTSForecast, a numerical–textual dual-channel framework: a trainable multi-layer perceptron encodes the numerical input, a frozen large language model (LLM) embeds the task instruction and the field labels, and a small head returns the load from the frozen decoder output. No backbone parameter is updated at any point. At each of six rolling origins training sees only a seven-day record of 94 samples, while the non-overlapping test windows supply 7824 held-out hourly points across four seasons, five seeds each. NTSForecast attains the lowest MAE, RMSE and MAPE of the methods compared (5.66 ± 0.04% against 5.68% for the strongest baseline), but that margin is small and sensitive to how serial dependence is handled; under scarce history the framework is competitive with that baseline, not superior to it. What the evidence does establish is the contribution of the frozen backbone. With architecture, trainable-parameter count, hyperparameters and data held identical, removing it costs 1.38 percentage points of MAPE. Backbone scale runs against a capacity argument; on a 0.5B-to-8B ladder the smallest pretrained backbone is the most accurate and becomes the reference configuration (1445 MB peak memory). Removing the textual channel costs 1.17 percentage points.