Predictive Maintenance of Distributed Energy Sensors through Cloud Telemetry: A Cross-Site Validation Agenda

Abstract

Distributed energy-monitoring networks collect large amounts of telemetry, yet seasonal load changes can be mistaken for equipment faults. An evaluation that overlooks these constraints may reach conclusions that cannot be reproduced. The research question is whether it is possible to distinguish actionable sensor degradation from benign demand fluctuations using time-aware predictive maintenance rules. Previous work provides context for sampling, input validation and study boundaries [3–7]. The planned analysis uses a prospective-style replay of multiseason sensor traces with independently logged fault and repair events. Prior work on storage operations provides technical context for validation [1], while complementary systems evidence motivates service-continuity checks [2]. Under matched evaluation conditions, the protocol compares fixed engineering thresholds, statistical change detection and a guarded machine-learning classifier. Methodological sources are used to specify transparent comparison and reporting practices [8–12]. The cross-site extension holds out an entire institution or deployment location rather than relying only on random test splits. Performance will be judged using alerts per sensor-month, detection lead time, missed faults and unnecessary maintenance decisions. Interpretation will account for sensor dropout, firmware changes, class imbalance and network interruption during peak loads. Between-site heterogeneity would be examined through prespecified subgroup estimates and transparent domain-shift diagnostics. The supplementary technical literature is used to identify plausible failure mechanisms [13–17]. No study result is assumed in advance; the analysis is designed to show both when the method helps and when it fails, with reporting context from the remaining sources [18–23].