# Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy
## 5 Digital Twin Technology for Energy Sector Development
### Software for Energy Prediction-Actual Software Cases
It was concluded that with the optimization work, iCD estimated energy savings and carbon reductions of 8.6% for Bertam City. This study provides a benchmark for potential carbon emission reductions and energy savings. Further optimization work could achieve a potential energy saving and carbon reduction of 15.7% by converting the existing HVAC system to one with better energy performance. In addition, the installation of solar PV panels in selected residential areas could result in a further 24.95% energy savings and carbon emission reduction. Combining these optimization measures, the total potential energy savings and carbon reductions for the city of Bertram could reach 49.25%. Using a bottom-up approach, [PERSON] et al. (2019) analyzed the energy consumption of various devices in detail by aggregating this data to predict the energy consumption of the entire building complex. The results of the study show that air-conditioning loads are the highest in residential buildings in Kuwait and are expected to grow exponentially from 2022 onwards, reaching a projected 60 TWh by 2040. Air-conditioning loads are the main component of energy consumption in residential buildings in Kuwait and are expected to grow exponentially. These results provide an opportunity to develop more effective energy policies as well as future energy efficiency initiatives. [PERSON] et al. (2018) used an assessment framework based on digital twin technology to conduct building modeling and energy analysis. The research methodology consisted mainly of creating operating schedules and generating operating schedules for lighting systems based on lecture schedules. The resultsof the study showed that the difference in energy savings between using the PIR system and the manager's manual control of the lighting was only 2%. In addition, LED lighting in most classrooms was found to be over-engineered and when illumination levels were adjusted to the appropriate level, energy savings of 46% could be achieved. Therefore, lighting control strategies can have a significant impact on energy consumption in educational buildings. The increase in the number and range of applications of building energy simulation software has also led to the importance of examining the variability and mutual consistency of simulation results between these tools. Each software has its own unique algorithms, modelling approach, and treatment of different building and environmental parameters, which can lead to significant differences in the results obtained when modelling the same problem. [PERSON] et al. [63] developed a BIM model using Revit and GBS (Green Building Studio) to carry out an energy analysis of a two-story service building in Lisbon, Portugal. To validate the results of the GBS energy analysis, the EC0.AP software and EnergyPlus outputs were used for comparison. In terms of total annual energy consumption, the results obtained from Autodesk Revit's Energy Analysis were approximately 61% higher than the results from ECO.AP Energy Simulator. In Section 7, the article points out that GBS is primarily applicable in the early stages of the building lifecycle, where the energy simulation results can support decisions aimed at improving the building's energy performance during the operational phase. The input limitations of GBS, mainly in the customization of the HVAC system, affect the building's performance and energy performance assessment under actual operating conditions. In the discussion section, the article discusses the differences between the results obtained by the two approaches, mainly due to the limitations in the customization of HVAC systems in Revit and the fact that the 12 HVAC solutions available in Revit are based on the Commercial Building Energy Consumption Survey (CBECS) in the US and Australia, which makes them unrepresentative of the Portuguese situation. In addition, the different energy simulation engines used by the two methods (GBS uses DOE-2 and ECO.AP uses EnergyPlus) may also lead to differences in the results. [PERSON] et al.