aceOptimizing solar and wind forecasting with iHow optimization algorithm and multi-scale attention networks
Deep learning models often encounter two key challenges in developing intelligent and scalable forecasting frameworks for renewable energy systems: input feature space dimensionality and sensitivity to hyperparameter settings. These limitations increase computational cost and compromise generalization and robustness. This paper presents a hybrid deep learning-optimization framework that leverages cognitively inspired metaheuristics to address these challenges, employing the Binary iHow Optimization Algorithm (bi…
Hybrid framework using Binary iHow for feature selection and iHow for hyperparameter tuning of a Multi-Scale Attention Network reduced forecasting error for wind and solar generation and improved computational scalability for smart-grid management.
Evidence
- Peer-reviewedScientific Reports2026-03-10
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Truvace Impact Record TRV-2026-0168, v1: “Optimizing solar and wind forecasting with iHow optimization algorithm and multi-scale attention networks.” Truvace, 2026-07-13. /record/TRV-2026-0168 (accessed at citation time). sha256 946a034fc279c03d…
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