Enhancing reliability of energy management software through predictive modeling and automated repair

DOI: 10.31673/2412-9070.2025.061212

Abstract

This research is conducted within the Department of Software Engineering for Power Industry, NTUU KPI and Foreign Expert Studio for Demand Response at the Shandong-Uzbekistan Technological Innovation Research Institute collaboration under the Project H20240943 Quality Assurance Project for Intelligent Energy Management Software Based on AI Methods and the De-velopment and Industrialization of Intelligent Grid Demand Response Technology Project.
Intelligent Energy Management Software (IEMS) plays a vital role in forecasting, optimization, and anomaly detection within modern energy infrastructures. However, evolving data distributions and heterogeneous deployment conditions introduce high risks of software defects and unstable behavior. This paper proposes a prediction–repair framework that unifies defect prediction with automated multi-level repair to ensure both accuracy and reliability. The prediction module employs hybrid models combining temporal and structural features, while the repair module operates at software, model, and system levels. Public datasets – NASA MDP and PROMISE for software defect prediction, NAB for anomaly detection, and UCI Energy for calibration assessment – are used for validation. Results show that the proposed method consistently outperforms baseline approaches, yielding F1-score improvements of 5–10 points on defect prediction and an 8-point gain on NAB anomaly detection (0.70 → 0.78). Calibration reliability also increases, reducing Expected Calibration Error to 0.032 and Negative Log Likelihood to 0.18. Furthermore, integrated repair improves recovery to 87% and reduces latency by 36% compared with single-level strategies. These findings demonstrate that coupling predictive modeling with automated repair enhances robustness and trustworthiness of IEMS under distributional shift, providing a practical route for reliable deployment in residential, commercial, and industrial contexts.

Keywords: intelligent energy management software (IEMS); defect prediction; automated repair; anomaly detection; calibration; robustness; software quality assurance.