Leveraging Deep Learning and Digital Twins to Improve Energy Performance of Buildings

Digital transformation in buildings accumulates massive operational data, which calls for smart solutions to utilize these data to improve energy performance. This study has proposed a solution, namely Deep Energy Twin, for integrating deep learning and digital twins to better understand building energy use and identify the potential for improving energy efficiency. Ontology was adopted to create parametric digital twins to provide consistency of data format across different systems in a building. Based on created digital twins and collected data, deep learning methods were used for performing data analytics to identify patterns and provide insights for energy optimization. As a demonstration, a case study was conducted in a public historic building in Norrköping, Sweden, to compare the performance of state-of-the-art deep learning architectures in building energy forecasting.

I. Introduction

Digital transformation in buildings has brought considerable opportunities to optimize their energy performance by integrating various advanced information and communication tech-nologies [1]. Among them, digital twin technology is today a powerful tool for building management. With continuously collected data [2], a digital twin reflects the latest status of its physical counterpart in nearly real-time [3]. In addition, more advanced data analysis applications, such as energy forecasting and predictive controls, can be developed based on the virtual model and data from meters, sensors, actuators, and control systems [4]. Deep learning has shown great potential in data analytics [5], [6]. Based on large amounts of the collected data, deep learning methods can be used to develop models for identifying patterns in operational data, such as making predictions about energy use and revealing the potential for energy optimization [7].