Towards automating the deployment of energy saving approaches in buildings

Abstract

Almost 32% of the total energy consumption in industrialized countries is used for electricity, heating, ventilation, and air-conditioning (HVAC) in buildings. Deploying Energy Management Systems (EMS) helps reducing energy use. Unfortunately it is a complex task that requires to identify the EMS inputs among thousands of sensors in a building. Since most of these sensors lack any labeling standard this is currently done manually. We aim to semi-automate this mapping task and address the problem of identifying EMS inputs with minimal user involvement. This is achieved by utilizing linguistic and semantic techniques for computing similarity values between labels of sensors and EMS inputs. Experiments show that our approach can be successfully applied to real-world data.