Energy Anomaly Detection with Forecasting and Deep Learning

Monitoring energy consumption data is essential to the everyday workings of power companies; a single uncaught incident outside the standards of normal use can result in financial loss. To minimize the repercussions of an uncaught error, the utilization of forecasting and machine learning can significantly improve the detection of such anomalies in dayto-day operations. This study covers power anomaly detection with the use of deep learning algorithms that have the capability of removing seasonality and trend from data, yielding residual values that are applied in a comparison to values generated from predictive analysis using recurrent neural networks (RNN). Data for this study is provided by Tennessee Valley Authority (TVA).