  Anomaly detection is a critical task that involves the identification of data
points that deviate from a predefined pattern, useful for fraud detection and
related activities. Various techniques are employed for anomaly detection, but
recent research indicates that deep learning methods, with their ability to
discern intricate data patterns, are well-suited for this task. This study
explores the use of Generative Adversarial Networks (GANs) for anomaly
detection in power generation plants. The dataset used in this investigation
comprises fuel consumption records obtained from power generation plants
operated by a telecommunications company. The data was initially collected in
response to observed irregularities in the fuel consumption patterns of the
generating sets situated at the company's base stations. The dataset was
divided into anomalous and normal data points based on specific variables, with
64.88% classified as normal and 35.12% as anomalous. An analysis of feature
importance, employing the random forest classifier, revealed that Running Time
Per Day exhibited the highest relative importance. A GANs model was trained and
fine-tuned both with and without data augmentation, with the goal of increasing
the dataset size to enhance performance. The generator model consisted of five
dense layers using the tanh activation function, while the discriminator
comprised six dense layers, each integrated with a dropout layer to prevent
overfitting. Following data augmentation, the model achieved an accuracy rate
of 98.99%, compared to 66.45% before augmentation. This demonstrates that the
model nearly perfectly classified data points into normal and anomalous
categories, with the augmented data significantly enhancing the GANs'
performance in anomaly detection. Consequently, this study recommends the use
of GANs, particularly when using large datasets, for effective anomaly
detection.
