Neural network autoencoders are deep learning architectures designed to compress and then reconstruct input data, using the resulting reconstruction error to flag anomalies. They are highly effective for monitoring complex, interconnected thermal systems where traditional, threshold-based alarm limits fail to detect multi-variable anomalies.