Artificial Intelligence-Driven Sensing Framework with Multimodal Sensor Importance Learning for Smart Energy Systems.

Area of Science:

• Energy Systems Engineering
• Artificial Intelligence
• Environmental Science

Background:

• Accelerated green energy development necessitates advanced wind power forecasting.
• Intelligent sensing technologies are increasingly integrated into energy systems.
• Conventional forecasting methods struggle with dynamic importance and stability in complex wind conditions.

Purpose of the Study:

• To propose a novel forecasting framework based on multimodal sensor importance perception.
• To address limitations in dynamic importance modeling and stability under complex wind conditions.
• To decode nonlinear dependencies between atmospheric drivers and turbine responses.

Main Methods:

• Developed a multimodal feature encoding architecture for unified temporal representations.
• Introduced a sensor-importance-aware attention mechanism and cross-modal relational modeling.
• Integrated prediction compensation and uncertainty characterization modules for enhanced robustness.

Main Results:

• Achieved Mean Absolute Error (MAE) of 30.48, Root Mean Square Error (RMSE) of 42.37, and Mean Absolute Percentage Error (MAPE) of 9.16%.
• Attained a coefficient of determination (R2) of 0.957, outperforming the Transformer baseline.
• Demonstrated superior error accumulation suppression in multi-horizon forecasting tasks.

Conclusions:

• The proposed framework effectively captures context-dependent nonlinear mappings in energy systems.
• Provides robust technical support for green energy dispatch and intelligent sensing applications.
• Advances the paradigm of multimodal sensor collaborative perception in wind power forecasting.