Understanding the adoption and usage of data analytics and simulation among building energy management professionals: A nationwide survey

Abstract

Improving the energy-efficiency of our building stock is critical to meeting our worldwide sustainability goals. In response to this need, two key tools have emerged to help engineers, building scientists and energy managers understand building energy usage and derive energy-efficiency solutions: data analytics and simulation. While both data analytics and simulation hold significant promise, we lack a clear understanding on the use, barriers and expectations of both in the building energy management decision-making process. This study conducts a nationwide survey of 448 building energy management professionals in the United States to help elucidate: 1) what impacts the adoption of data analytics and simulation among building energy management professionals; 2) in what phases of the building energy management decision-making process are data analytics and simulation currently used; and 3) what are the barriers of use for data analytics and simulation and how can they be improved to better support building energy management decision-making. Overall, results indicate that professional domain plays a large role in associating the uses, barriers and expectations for data analytics and simulation. Results also indicate that data analytics and simulation could be coupled to leverage functionality as they are used in similar phases of the decision-making process. Lastly, results point to opportunities for improving the applicability of data analytics and simulation tools as well as training for both. In the end, this study aims to provide a quantitative basis for improving the efficacy and integration of data analytics and simulation in the building energy management domain.