Сross-domain adaptation and trusted quality assurance of intelligent energy management software

• Datum: 2026
• Hauptverfasser: Жихай, Ванг, Верлань, А. А.
• Format: Artikel
• Sprache: Englisch
• Veröffentlicht: Інститут проблем реєстрації інформації НАН України 2026
• Schlagworte: Intelligent Energy Management Software (IEMS); Cross-Domain Adaptation; Transfer Learning; Domain-Adversarial Training; Trustworthy Artificial Intelligence; Software Quality Assurance; Calibration; Explainability
• Online Zugang: https://drsp.ipri.kiev.ua/article/view/363165
• Назва журналу: Data Recording, Storage & Processing

Intelligent Energy Management Software must ensure reliable operation across heterogeneous domains where data distributions and system environments frequently vary. This paper introduces a cross-domain adaptation and trusted quality assurance framework that combines transfer learning, adversarial domain alignment, and calibration based reliability assessment, validated on NASA MDP, PROMISE, NAB, and UCI energy benchmarks. Compared with strong baselines such as Random Forest, Convolutional Neural Networks, and Gated Recurrent Units, the proposed method achieves consistent improvements, yielding absolute F1 score gains of 5 to 10 points on defect prediction (NASA MDP, PROMISE) and an increase of 8 points on anomaly detection (NAB, from 0.70 to 0.78), while reducing Expected Calibration Error to 0.032 (a 22 to 42 percent reduction relative to Bayesian CNN baselines) and Negative Log Likelihood to 0.18, thereby demonstrating that integrating cross-domain adaptation with rigorous quality assurance mechanisms significantly enhances both predictive performance and reliability in real world IEMS deployments.