One of the critical factors that drive the economic development of a country
and guarantee the sustainability of its industries is the constant availability
of electricity. This is usually provided by the national electric grid.
However, in developing countries where companies are emerging on a constant
basis including telecommunication industries, those are still experiencing a
non-stable electricity supply. Therefore, they have to rely on generators to
guarantee their full functionality. Those generators depend on fuel to function
and the rate of consumption gets usually high, if not monitored properly.
Monitoring operation is usually carried out by a (non-expert) human. In some
cases, this could be a tedious process, as some companies have reported an
exaggerated high consumption rate. This work proposes a label assisted
autoencoder for anomaly detection in the fuel consumed by power generating
plants. In addition to the autoencoder model, we added a labelling assistance
module that checks if an observation is labelled, the label is used to check
the veracity of the corresponding anomaly classification given a threshold. A
consensus is then reached on whether training should stop or whether the
threshold should be updated or the training should continue with the search for
hyper-parameters. Results show that the proposed model is highly efficient for
reading anomalies with a detection accuracy of $97.20\%$ which outperforms the
existing model of $96.1\%$ accuracy trained on the same dataset. In addition,
the proposed model is able to classify the anomalies according to their degree
of severity.

提出了基于标签辅助的自动编码器模型用于发现发电厂燃料消耗的异常，检测准确率为 97.20％，可以根据程度对异常进行分类，相较于已有模型较优。