Energy Modelling of Testbed KTH A first step of a Digital Twin

Abstract

This thesis focuses on the modeling and calibration of a residential apartment to accurately
predict its thermal performance and energy consumption. Building energy simulations are
essential tools for optimizing design, improving energy efficiency, and ensuring occupant
comfort. However, the reliability of these simulations depends on the accuracy of the input
data and the assumptions made during the modeling process. In this study, a detailed simulation
model of Testbed KTH was developed, incorporating factors such as internal heat gains, weather
conditions, and HVAC system performance. The model was then calibrated by comparing
simulated temperatures with measured data and comparing the heating energy delivered by
the AHU, allowing for the identification and correction of discrepancies.
Calibration is a critical step in ensuring that the simulation reflects real-world conditions.
Without it, the model may overestimate or underestimate heating and cooling demands, leading
to inefficient system design and operation. The findings of this thesis highlight the importance
of refining key model inputs—such as internal heat gains and weather data—to improve
accuracy. The calibrated model provides a reliable tool for predicting energy usage and indoor
temperatures, offering insights that can guide the design of more energy-efficient buildings and
better inform decisions on retrofitting existing structures. This work underscores the necessity
of a rigorous calibration process to achieve realistic and actionable simulation outcomes.
This work aims to provide a fully developed model which can be further developed to build
a digital twin of Testbed KTH by utilising the APIs offered by the simulation tool IDA ICE.