Article: Multimodal knowledge fusion and intelligent generation model in decision support systems for energy industry Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.62 pp.46 - 69 Abstract: Faced with the challenges of complexity and uncertainty of multi-source heterogeneous data in energy industry decision support systems and the dynamic environment adaptation needs brought about by smart grids and renewable energy access, this study aims to build an adaptive, strong and robust multi-modal knowledge fusion and intelligent generation model to improve the accuracy and reliability of decision-making. By innovatively integrating hypergraph attention networks to achieve multi-modal feature fusion, cloud model quantification of data uncertainty, and reinforcement learning framework-driven intelligent policy generation, the model achieves an accuracy rate of 93.5%, an F1 score of 92.8% and RMSE 0.08 in performance tests. In addition, robustness tests show that the model has a change rate of only 3.7% under 30% noise, the decision delay is optimised to 65 milliseconds, and the accuracy rate increases to 89.2% after fine-tuning in cross-scenario generalisation. Overall, the model effectively solves the semantic gap and data quality problems, and provides efficient support for energy scheduling, fault prediction and other scenarios. Inderscience Publishers - linking academia, business and industry through research

Title: Multimodal knowledge fusion and intelligent generation model in decision support systems for energy industry

Authors: Wenke Li; Xinping Miao; Ruoyan Dong; Yue Tian; Qingbo Kong

Addresses: Guizhou Power Grid Co., Ltd. Digital Intelligence Operation Center, GuiYang, GuiZhou, 551400, China ' Guizhou Power Grid Co., Ltd. Digital Intelligence Operation Center, GuiYang, GuiZhou, 551400, China ' Guizhou Power Grid Co., Ltd. Digital Intelligence Operation Center, GuiYang, GuiZhou, 551400, China ' Guizhou Power Grid Co., Ltd. Digital Intelligence Operation Center, GuiYang, GuiZhou, 551400, China ' Guizhou Power Grid Co., Ltd. Digital Intelligence Operation Center, GuiYang, GuiZhou, 551400, China

Abstract: Faced with the challenges of complexity and uncertainty of multi-source heterogeneous data in energy industry decision support systems and the dynamic environment adaptation needs brought about by smart grids and renewable energy access, this study aims to build an adaptive, strong and robust multi-modal knowledge fusion and intelligent generation model to improve the accuracy and reliability of decision-making. By innovatively integrating hypergraph attention networks to achieve multi-modal feature fusion, cloud model quantification of data uncertainty, and reinforcement learning framework-driven intelligent policy generation, the model achieves an accuracy rate of 93.5%, an F1 score of 92.8% and RMSE 0.08 in performance tests. In addition, robustness tests show that the model has a change rate of only 3.7% under 30% noise, the decision delay is optimised to 65 milliseconds, and the accuracy rate increases to 89.2% after fine-tuning in cross-scenario generalisation. Overall, the model effectively solves the semantic gap and data quality problems, and provides efficient support for energy scheduling, fault prediction and other scenarios.

Keywords: energy industry; decision-making; multi-modal; knowledge fusion; intelligent generation.

DOI: 10.1504/IJICT.2026.153993

International Journal of Information and Communication Technology, 2026 Vol.27 No.62, pp.46 - 69

Received: 09 Jan 2026
Accepted: 27 Feb 2026
Published online: 09 Jun 2026 *

Title: Multimodal knowledge fusion and intelligent generation model in decision support systems for energy industry

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