The explosive growth of artificial intelligence is exponentially escalating computational demand, inflating data-centre energy use and carbon emissions, and spurring rapid deployment of green data centres to relieve resource and environmental stress. Achieving sub-minute orchestration of renewables, smart grids, storage, and thermo-fluid loads, while minimising PUE and lifecycle carbon intensity, hinges on accurate load forecasting. Existing rule-based or thermodynamic methods are subjective and costly. Although typical deep time-series networks perform well on large-scale datasets, fail in cold-start, fragmented, or drifting environments typical of nascent green data centres. We introduce HyperLoad, a cross-modal framework that exploits large-scale pre-trained language models (LLMs) to overcome data scarcity. In the alignment stage, textual priors and time-series attributes are mapped to a common latent space, maximizing prior-knowledge utility. In the modeling stage, domain-aligned priors are injected through adaptive prefix tuning, enabling rapid scenario adaptation, while a global interactive self-attention module captures cross-device temporal dependencies. The public GreenData dataset is released for benchmarking. Under both data-sufficient and data-scarce regimes, HyperLoad consistently surpasses state-of-the-art baselines, demonstrating its practicality for sustainable data-centre management.