  Power demand forecasting is a crucial and challenging task for new power
system and integrated energy system. However, as public feature databases and
the theoretical mechanism of power demand changes are unavailable, the known
features of power demand fluctuation are much limited. Recently, multimodal
learning approaches have shown great vitality in machine learning and AIGC. In
this paper, we interact two modal data and propose a textual-knowledge-guided
numerical feature discovery (TKNFD) method for short-term power demand
forecasting. TKNFD extensively accumulates qualitative textual knowledge,
expands it into a candidate feature-type set, collects numerical data of these
features, and eventually builds four-dimensional multivariate source-tracking
databases (4DM-STDs). Next, TKNFD presents a two-level quantitative feature
identification strategy independent of forecasting models, finds 43-48
features, and systematically analyses feature contribution and dependency
correlation. Benchmark experiments in two different regions around the world
demonstrate that the forecasting accuracy of TKNFD-discovered features reliably
outperforms that of SoTA feature schemes by 16.84% to 36.36% MAPE. In
particular, TKNFD reveals many unknown features, especially several dominant
features in the unknown energy and astronomical dimensions, which extend the
knowledge on the origin of strong randomness and non-linearity in power demand
fluctuation. Besides, 4DM-STDs can serve as public baseline databases.
