A resource-efficient machine learning framework for real-time non-intrusive load monitoring and performance optimization in solar-powered aviation systems

Abstract

The integration of non-intrusive load monitoring (NILM) into solar-powered aviation systems presents a transformative approach for achieving sustainable, lightweight, and intelligent flight operations. However, the sector’s stringent constraints on weight, latency, and computational resources pose critical challenges to real-time NILM deployment. This study develops a resource-efficient machine learning framework that systematically evaluates six machine learning (ML) and deep learning (DL) models using high-resolution (200 kHz) power data that capture both transient and steady-state load characteristics across all flight phases. Advanced preprocessing techniques—comprising moving average smoothing and non-overlapping downsampling—were applied to suppress noise while preserving essential features. The comparative analysis reveals that K-Nearest neighbors (KNN) delivers the most effective balance between accuracy and computational cost, achieving an R² of 0.9403 with an execution time of 0.20 s, substantially outperforming ensemble models such as random forest (RF) and XGBoost in real-time feasibility. Conversely, the hybrid CNN-LSTM architecture attained the lowest mean squared error (MSE = 0.0048) and superior temporal sensitivity but required 271.53 s, demonstrating its suitability for offline analysis rather than onboard deployment. Through comprehensive hardware-in-the-loop validation using Opal-RT and Launchpad-F28379D DSP controllers, the framework verified appliance-level disaggregation accuracy under dynamic flight scenarios. The findings underscore a critical accuracy–efficiency trade-off in NILM model selection, establishing that traditional ML algorithms can outperform complex DL models when optimized for real-time, resource-constrained environments. This research provides actionable design insights for next-generation solar-powered aircraft energy management systems, demonstrating that model selection must prioritize computational efficiency, predictive reliability, and real-time responsiveness to enable sustainable and intelligent aviation energy control.