Modern buildings increasingly integrate local energy generation, consumption, and storage, often involving multiple energy carriers such as electricity and thermal energy. This complexity creates opportunities for cost-efficient operation based on accurate forecasts of key energy parameters such as the cooling demand. However, generating such forecasts on a building-level can be challenging, as individual buildings likely exhibit highly specific consumption patterns and often offer only limited historical data. Time Series Foundation Models (TSFMs) offer a promising solution to this problem due to their ability to generalise across forecasting tasks and adapt to new domains with minimal data via fine-tuning. This study evaluates three state-of-the-art TSFMs (MOIRAI, MOIRAI-MoE and Chronos) in both zero-shot and fine-tuned settings. The models are tested on real-world energy consumption data from a split-type cooling unit used in a server room, spanning a period of less than four months. Outdoor air temperature is included as a covariate to assess its impact on prediction accuracy. Results are compared to two baseline models. Our findings show that Chronos, when incorporating outdoor air temperature, achieves a substantial improvement in forecasting accuracy for three-day ahead forecasts, reducing the Mean Absolute Percentage Error (MAPE) to as low as 2.57 %. In contrast, MOIRAI and MOIRAI-MoE show no significant benefit from the inclusion of temperature information. Overall, the study demonstrates that TSFMs represent a promising alternative for cooling demand forecasting in Section 5. Section 6 summarises the findings and outlines future research directions.
Kreusel et al. (Wed,) studied this question.