Assessing Electricity Demand Forecasting with Exogenous Data in Time Series Foundation Models
This work addresses the problem of effectively leveraging exogenous features in time-series forecasting for electricity demand, highlighting domain-specific needs, but it is incremental as it primarily compares existing models without introducing new methods.
This paper evaluated time-series foundation models for electricity demand forecasting with exogenous data, finding that while Chronos-2 performed best among foundation models in zero-shot settings, a simple LSTM baseline often outperformed them in stable climates like Singapore, particularly for short-term predictions.
Time-series foundation models have emerged as a new paradigm for forecasting, yet their ability to effectively leverage exogenous features -- critical for electricity demand forecasting -- remains unclear. This paper empirically evaluates foundation models capable of modeling cross-channel correlations against a baseline LSTM with reversible instance normalization across Singaporean and Australian electricity markets at hourly and daily granularities. We systematically assess MOIRAI, MOMENT, TinyTimeMixers, ChronosX, and Chronos-2 under three feature configurations: all features, selected features, and target-only. Our findings reveal highly variable effectiveness: while Chronos-2 achieves the best performance among foundation models (in zero-shot settings), the simple baseline frequently outperforms all foundation models in Singapore's stable climate, particularly for short-term horizons. Model architecture proves critical, with synergistic architectural implementations (TTM's channel-mixing, Chronos-2's grouped attention) consistently leveraging exogenous features, while other approaches show inconsistent benefits. Geographic context emerges as equally important, with foundation models demonstrating advantages primarily in variable climates. These results challenge assumptions about universal foundation model superiority and highlight the need for domain-specific models, specifically in the energy domain.