  Concentrated Solar Power (CSP) plants store energy by heating a storage
medium with an array of mirrors that focus sunlight onto solar receivers atop a
central tower. Operating at high temperatures these receivers face risks such
as freezing, deformation, and corrosion, leading to operational failures,
downtime, or costly equipment damage. We study the problem of anomaly detection
(AD) in sequences of thermal images collected over a year from an operational
CSP plant. These images are captured at irregular intervals ranging from one to
five minutes throughout the day by infrared cameras mounted on solar receivers.
Our goal is to develop a method to extract useful representations from
high-dimensional thermal images for AD. It should be able to handle temporal
features of the data, which include irregularity, temporal dependency between
images and non-stationarity due to a strong daily seasonal pattern. The
co-occurrence of low-temperature anomalies that resemble normal images from the
start and the end of the operational cycle with high-temperature anomalies
poses an additional challenge. We first evaluate state-of-the-art deep
image-based AD methods, which have been shown to be effective in deriving
meaningful image representations for the detection of anomalies. Then, we
introduce a forecasting-based AD method that predicts future thermal images
from past sequences and timestamps via a deep sequence model. This method
effectively captures specific temporal data features and distinguishes between
difficult-to-detect temperature-based anomalies. Our experiments demonstrate
the effectiveness of our approach compared to multiple SOTA baselines across
multiple evaluation metrics. We have also successfully deployed our solution on
five months of unseen data, providing critical insights for the maintenance of
the CSP plant. Our code is available at: https://tinyurl.com/ForecastAD
