ArticleScientific reports2026
The future of power forecasting: neuromorphic-axolotl hybrid intelligence revolutionizing grid operations through bio-inspired missing data mastery.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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7 authors.
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Abstract
Electric power consumption forecasting faces critical challenges from missing data that severely compromise prediction accuracy and grid stability. This paper introduces a bio-neutrosophic intelligence framework integrating four groundbreaking innovations: (1) the first application of neutrosophic set theory specifically tailored for power consumption data imputation, simultaneously handling truth, indeterminacy, and falsehood memberships within temporal reconstruction processes; (2) novel axolotl-inspired regenerative mechanisms modeling the remarkable tissue reconstruction capabilities of Ambystoma mexicanum for adaptive missing data recovery; (3) the Bald Uakari metaheuristic algorithm—a new bio-inspired optimization technique based on territorial dynamics and social foraging strategies of Cacajou calvus primates with proven convergence guarantees; and (4) an integrated multi-objective optimization framework synergistically combining missing data imputation and feature selection. Extensive experimental validation using seven international datasets (52,416-4,370,000 observations) across diverse geographical regions with systematic missing data scenarios (5%-40% rates) demonstrates exceptional performance: 31.2% improvement in overall forecasting accuracy, 23.7% reduction in reconstruction error, and 28.9% RMSE reduction for LSTM networks. The framework achieves 82.3% cross-domain transfer efficiency across industrial, commercial, renewable microgrid, and EV charging environments without retraining. Statistical significance testing with Bonferroni, Benjamini-Hochberg, and Holm-Bonferroni corrections confirms all improvements are statistically significant (p < 0.001) with large effect sizes (Cohen’s d > 1.6). Comprehensive deployment analysis demonstrates practical applicability with 41.4 MB memory footprint for edge devices, 8,967 observations/second throughput, and robust performance under real-world MNAR and sensor-correlated outage scenarios, establishing new benchmarks for intelligent power system forecasting.
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