Energy-saving decision making framework for HVAC with usage logs

Abstract Commercial and residential buildings account for 40% of total energy usage in the United Sates. Efforts have been made to better manage energy through the implementation of building energy management systems. In particular, heating, ventilating, and air conditioning consume 50% of energy used in buildings. The system air-conditioner, an individually as well as centrally controlled HVAC system, is widely used across Asia. As energy cost increases, building managers often take building-wide energy saving measures, such as intermittent indoor unit shut-down and setting units to uncomfortable temperatures. In this paper, we argue that a smarter approach is possible. Analyzing a large amount of data collected from indoor units, we see that these units have varying levels of energy consumption and that we can identify those that consume more energy than others, resulting in individual measures for each. We propose a business intelligence framework that allows us to see why certain rooms consume more energy than others through three variables: hours of power-on, hours of thermo-on, and a timeframe of power-on. All variables are visualized, providing building managers with an intuitive understanding of their buildings’ energy consumption. We used real-world data to validate the framework and received positive feedbacks from domain experts.