With the increasing global focus on renewable energy and the growing proportion of renewable power in the energy mix, accurate forecasting of renewable power demand has become crucial. This study addresses this challenge by proposing a multimodal information fusion approach that integrates time series data and textual data to leverage complementary information from heterogeneous sources. We develop a hybrid predictive model combining CNN and Bi-GRU architectures. First, time series data (e.g., historical power generation) and textual data (e.g., policy documents) are preprocessed through normalization and tokenization. Next, CNNs extract spatial features from both data modalities, which are fused via concatenation. The fused features are then fed into a Bi-GRU network to capture temporal dependencies, ultimately forming a robust CNN-Bi-GRU model. Comparative experiments with ARIMA, standalone GRU, and EEMD-ARIMA (a hybrid model combining ensemble empirical mode decomposition with ARIMA) demonstrate the superiority of our approach in both short- and long-term forecasting tasks on the same dataset. This research offers a potential framework to enhance renewable power demand prediction, supporting the industry’s sustainable growth and practical applications.