# 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
The research methodology includes the use of GIS technology to collect geometric information on buildings, solar radiation analysis, and energy demand analysis using the degree-day method. In addition, the model incorporates European standard methodologies and has been validated with several case studies. The results show that the model is a useful energy planning tool for predicting the energy demand of residential buildings and regions, as well as the impact of climate change on their energy demand and the consequences of countermeasures such as retrofitting. [PERSON] et al. [PERSON] et al. (2018) investigated the pattern of window-to-wall ratio (WWR) on energy demand in residential buildings in different regions of China. The research methodology focuses on theoretical analysis and simulation calculations. A mathematical model of the building energy balance was developed to investigate how the window-to-wall ratio (WWR) affects the energy demand of a building. Simulations were then carried out using DeST software to verify the accuracy of this theoretical analysis method. The results show that the window-to-wall ratio (WWR) has a significant impact on the annual cooling and heating loads. The cooling load grows faster than the thermal load. The window-to-wall ratio (WWR) is an inherent geometric characteristic of the building itself, and this theoretical approach allows an accurate assessment of how the window-to-wall ratio (WWR) affects the building's energy demand. In addition, simulations where the absolute reduction in energy demand is closely domain with local climatic conditions are included, while the rate of change in heating and cooling loads varies little between cities. [PERSON] et al. (2016) assessed the impact of climate change on building energy demand in Mexico by collecting climatic data and then calculating and analyzing this data. Meteoromr software was used to collect climatic data including ambient temperature, wind speed and direction, relative humidity, and solar radiation to study the effects of climate change on building energy demand in Mexico, particularly for single-family dwellings based on representative concentration pathways. [PERSON] et al. (2019) quantified, compared, and improved building design options through an integrated approach of BIM and energy simulation, aiming to reduce the carbon footprint and energy consumption of a residential building in Melbourne. The study used Autodesk Revit as the BIM tool, FirstRate5 as the energy assessment tool and sensitivity analysis with the @Risk Palisade decision tool to optimize operational energy efficiency. The study methodology included the use of BIM, energy rating tools, life cycle assessment to quantify, compare, and improve building design options, and sensitivity analysis to optimize operational energy efficiency. The results show that the use of passive design strategies such as orientation, shading, sealing, glazing, and insulation can significantly reduce the need for artificial heating and cooling systems, accounting for 40% of the total energy use in residential buildings. Insulation contributes 4% of the carbon emissions and 7% of the primary energy demand, although it only accounts for 1% of the total mass. Therefore, an integrated workflow of BIM and energy simulation software allows for in-depth analysis of building solutions and identification of strategies to improve energy efficiency and environmental impact. The researchers (2018) used digital twin technology to study carbon emissions and energy consumption in the Malaysian city of Bertam. The research methodology involved using digital twin technology to create a model of Bertam City and carry out simulations of energy performance. The simulations took into account digital twin data on factors such as location, solar radiation, building run times, glazing ratios, building materials, and building occupancy. The energy consumption and carbon emissions of the digital twin city were estimated based on four planning scenarios.