Short-term anomaly detection in gas consumption through ARIMA and Artificial Neural Network forecast

This paper presents a method for finding anomalies in gas consumption that can identify causes of wasting energy. Our approach is to use historical data on local weather, building usage and gas consumption, to predict the gas consumption for a particular day and time. The prediction is a combination of auto-regression and artificial neural networks and anomalies, relatively large deviations from the predicted gas consumption values, are detected. These can point to incorrect settings of controls, faults in installations or incorrect use of the building.