Early-warning application for real-time detection of energy consumption anomalies in buildings

Abstract

Energy consumption data must be presented to office occupants to encourage them to save energy when in their office buildings. Therefore, this work develops an early warning application (EWA) that intelligently analyzes electricity consumption and provides a real-time visualization of anomalous consumption based on data from smart meters and sensors to various stakeholders. Although smart meters collect massive amounts of data from different sources, many systems cannot analyze and informatively present rapidly collected data. Accordingly, they do not motivate people to adopt energy-saving behaviors. The contribution of this study is to design an EWA architecture that visually presents real-time anomalous power consumption in an office space based on data that are obtained from various instruments (smart meters and sensors) to office occupants. The anomalous consumption of the EWA dashboard is designed to ensure that office occupants with limited technical skills understand the presented energy consumption data. The collected anomaly data provide post-occupancy information. Electricity consumption data from smart meters and sensors in a real office space are used to demonstrate the effectiveness of the proposed EWA. A building manager can use archived anomaly data to audit energy consumption, to produce an energy reduction policy, and to support a retrofitting strategy.