Towards Multi-Scenario Forecasting of Building Electricity Loads with Multimodal Data

The rapid urbanization process has significantly increased building energy consumption and carbon emissions, making reliable electricity load forecasting crucial for energy management. However, accurate load forecasting faces three key challenges: (1) complex impact of multimodal data, (2) inter-building semantical relationships, and (3) uncertainty modeling of load patterns. To address these, we propose MMLoad, a novel diffusion-based multimodal framework for multi-scenario building load forecasting with three innovations: (i) a Multimodal Data Enhancement Pipeline generating rich building descriptions using LLMs and integrating temporal factors to analyze multimodal impacts; (ii) a Cross-modal Relation Encoder discovering latent interdependencies through hierarchical fusion, projecting buildings into a unified spatio-temporal (ST) embedding space; and (iii) a Scenario-Conditioned Diffusion Generator employing transformer-based denoising with Scenario-Adaptive Normalization (SAN) for diverse trajectory generation with uncertainty quantification. Experiments show MMLoad outperforms state-of-the-art baselines in accuracy while generating plausible future scenarios, establishing a new paradigm for multimodal learning in smart energy systems.