Geothermal energy plays an increasingly important role in decarbonizing heating, cooling, and power production. As geothermal systems operate under extreme temperatures, pressures, and subsurface uncertainties, maintaining reliable operation is critical to sustaining a continuous energy supply and reducing the total cost of ownership. Ensuring the safe and efficient operation of geothermal plants therefore requires continuous monitoring of complex, multivariate sensor streams to detect equipment degradation and anticipate operational failures before they occur. This often relies on separate specialized physics-based and machine learning models for each task, with sparse labels and inter-site variability limiting generalization.

In this work, we explore the application of state-of-the-art Time Series Foundation Models (TSFMs) as a unified alternative for both forecasting and anomaly detection in geothermal operations. We present a geothermal-specific benchmark for time series modeling and conduct a systematic comparative evaluation of conventional machine and deep learning baselines against pretrained TSFMs under zero-shot conditions. The results demonstrate that, in forecasting tasks, covariate-aware TSFMs, particularly Chronos, consistently outperform all trained baselines, achieving 22–35% lower RMSE across horizons. For anomaly detection, we evaluate multiple detection strategies and find that performance is influenced more strongly by the choice of detection strategy and the availability of labeled fault data than by forecasting accuracy alone, with TSFM embeddings consistently encoding system information and enabling effective anomaly detection under labeled conditions.

These findings establish TSFMs as a promising foundation for intelligent condition monitoring in geothermal and broader industrial time series applications, while highlighting the importance of explicit covariate modeling for this class of systems. ... Read more

Object detection heavily relies on accurate annotations, which are costly to obtain but crucial for model performance. Annotation errors can severely impact the reliability of detection models. In response to this challenge, we introduce EnsembAudit (EA), a novel framework designed to autonomously identify and rectify common labeling errors, thereby reducing annotation efforts. EA leverages ensemble techniques such as Threshold Voting for error identification and Non-Maximum Suppression for error rectification.

This paper evaluates EA across various noise levels and types of labeling errors to assess its effectiveness. Our experiments demonstrate that EA excels in detecting and rectifying errors in datasets with significant noise, achieving an approximate 20% reduction in errors. However, its efficacy diminishes when applied to datasets with minimal noise. This study highlights EA's potential in enhancing annotation quality and improving the robustness of object detection applications. ... Read more