The future of power forecasting: neuromorphic-axolotl hybrid intelligence revolutionizing grid operations through bio-inspired missing data mastery

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.