Optimizing solar and wind forecasting with iHow optimization algorithm and multi-scale attention networks
Marwa Radwan; Abdelhameed Ibrahim; M. A. Abdelsalam; Amel Ali Alhussan; Ebrahim Abdulla Mattar; El-Sayed M. El-Kenawy · 2026 · Scientific Reports
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
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 (biHOW) for feature selection and its continuous counterpart, iHOW, for hyperparameter
Abstract by Marwa Radwan; Abdelhameed Ibrahim; M. A. Abdelsalam; Amel Ali Alhussan; Ebrahim Abdulla Mattar; El-Sayed M. El-Kenawy, Scientific Reports (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41598-026-39632-y