Digital Twin for Grey Box modeling of Multistory residential building
  thermal dynamics
Lina Morkunaite Justas Kardoka Darius Pupeikis Paris Fokaides Vangelis Angelakis
Buildings energy efficiency is a widely researched topic, which is rapidly
gaining popularity due to rising environmental concerns and the need for energy
independence. In Northern Europe heating energy alone accounts for up to 70
percent of the total building energy consumption. Industry 4.0 technologies
such as IoT, big data, cloud computing and machine learning, along with the
creation of predictive and proactive digital twins, can help to reduce this
number. However, buildings thermal dynamics is a very complex process that
depends on many variables. As a result, commonly used physics-based white box
models are time-consuming and require vast expertise. On the contrary, black
box forecasting models, which rely primarily on building energy consumption
data, lack fundamental insights and hinder re-use. In this study we propose an
architecture to facilitate grey box modelling of building thermal dynamics
while integrating real time IoT data with 3D representation of buildings. The
architecture is validated in a case study creating a digital twin platform that
enables users to define the thermal dynamics of buildings based on physical
laws and real data, thus facilitating informed decision making for the best
heating energy optimization strategy. Also, the created user interface enables
stakeholders such as facility managers, energy providers or governing bodies to
analyse, compare and evaluate buildings thermal dynamics without extensive
expertise or time resources.