Comment feedback optimization algorithm (CFOA)-optimized spatio-temporal graph networks for biomass forecasting and predictive maintenance support in renewable energy systems

Abstract

With the increasing demand for sustainable energy solutions, accurate biomass forecasting has become essential for optimizing renewable energy utilization and supporting system-level predictive maintenance and operational decision-making. However, the complexity and high dimensionality of biomass energy data pose significant challenges for conventional forecasting approaches. To address these challenges, this study proposes a framework integrating Spatio-Temporal Graph Convolutional Networks (STGCN) with metaheuristic optimization techniques. The proposed approach improves forecasting performance by capturing spatio-temporal dependencies while optimizing feature selection and model hyperparameters. Initially, the baseline STGCN model achieved an MSE of 0.0025, RMSE of 0.0500, and $$R^2$$ of 0.8317. Feature selection using the Binary Comment Feedback Optimization Algorithm (bCFOA) reduced redundancy and improved the forecasting results to an MSE of 0.0018, RMSE of 0.04243, and $$R^2$$ of 0.912. Further improvement through CFOA-based hyperparameter optimization produced the best performance, achieving an MSE of $$0.000554 \pm 0.000012$$ , RMSE of $$0.02354 \pm 0.00028$$ , MAE of $$0.00410 \pm 0.00009$$ , and $$R^2$$ of $$0.981 \pm 0.002$$ . The results demonstrate that combining graph-based spatio-temporal learning with evolutionary optimization significantly improves forecasting accuracy and model generalization. Moreover, the proposed CFOA-STGCN framework provides a scalable, interpretable, and computationally efficient solution suitable for biomass energy forecasting and operational decision-support systems.