Transfer Learning Signals, Demand Forecasting, and Regional Energy Markets: Mixed Evaluation

Abstract

The integration of renewable energy sources and the ongoing decentralization of power grids have introduced unprecedented volatility into regional energy markets. Accurate demand forecasting is essential for ensuring grid stability, optimizing energy dispatch, and facilitating economic trading. However, newly established regional markets or smaller grid nodes frequently suffer from severe data scarcity, rendering traditional deep learning models ineffective due to their reliance on vast amounts of historical data. This paper presents a comprehensive mixed evaluation of transfer learning signals applied to demand forecasting within constrained regional energy markets. By leveraging data-rich source domains, we investigate the efficacy of transferring learned representations to data-poor target domains. The study proposes a domain adaptation framework that isolates and aligns temporal features, seasonal variations, and meteorological impacts across disparate geographical regions. Extensive empirical evaluations demonstrate that the strategic utilization of transfer learning significantly reduces forecasting errors in the target domains while accelerating model convergence. The analysis highlights the specific types of signals most conducive to cross-regional transfer, such as macroscopic seasonal trends, contrasting them with localized micro-patterns that hinder model generalization. These findings provide critical insights for energy economists and grid operators seeking to deploy robust forecasting infrastructure in emerging, data-constrained energy markets.