Monitor: An Abnormality Detection Approach in Buildings Energy Consumption

Abstract

With the growth of smart cities, more buildings are now being instrumented with smart meters for providing better energy efficiency for sustainable development. Buildings consume around 39% of electrical energy worldwide and studies report that wasteful consumer behavior such as forgetting to switch off an appliance after use or using an appliance with misconfigured settings adds about one-third to buildings consumption. These instances result in deviations in energy consumption as compared to its normal consumption and are called as abnormalities. Detecting such abnormalities is important for reducing energy wastage. Existing methods detect abnormalities by analyzing smart meter data, however, they result in a high number of false positive alarms. This inaccuracy results in ignoring the alarms by building administrators which also affects genuine alarms. Thus, reducing the false positive alarms and making detection algorithms more accurate is a major aim. In this paper, we present our novel approach, called Monitor, which first identifies patterns in past consumption data and then uses these patterns to detect abnormalities. Our approach requires smart meter data only and reduces the rate of false positive alarms considerably. We have evaluated our approach on 16 weeks smart meter data of real world buildings. The comparison of this approach with existing approaches shows that our approach improves the accuracy by up to 24% in best scenario and on average by 14%. This improvement in accuracy reduces the rate of false positive alarms significantly and makes it more suitable for real-world deployments.