A Multi-Load Forecasting Method for Fuel Cell Microgrids Based on Deep Belief Networks

Accurate load prediction is critical for optimizing energy dispatch and ensuring reliable operation within fuel cell microgrids. Addressing the limitations of conventional forecasting methods when applied to the complex, multi-faceted load profiles inherent in these systems, this paper introduces a novel multi-dimensional load forecasting approach based on deep belief networks (DBNs). This DBN model effectively extracts high-order nonlinear characteristics from load data and integrates diverse information sources, enabling precise forecasting of electricity, hydrogen, and heat demands. Furthermore, genetic algorithms and mutual information are utilized to optimize feature selection. To comprehensively evaluate the model's performance, particularly considering the unique attributes of fuel cell microgrids, we propose three specific indices: a Comprehensive Error Improvement Index (CEII), a Response Speed Adaptability Index (S), and a Fluctuation Characteristic Matching Index (F). Experimental results demonstrate that the proposed DBN-based method significantly outperforms traditional techniques in terms of forecasting accuracy, response speed, and the ability to match load fluctuation characteristics. This research offers crucial technical support for the optimized operation of fuel cell microgrids