# 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 results showed that inter-building effects (IBE) have a significant impact on building energy consumption, which is related to factors such as building height, shape, orientation, window-to-wall ratio, window U-value, and solar heat gain coefficient. [PERSON] et al. [80] used time-series energy data from 240 office buildings in Shanghai to identify unnecessary building energy consumption and thus improve energy efficiency. Three methods of extracting runtimes from time series data are proposed in conjunction with simulations using EnergyPlus, and the use of cumulative histogram methods to extract building operating characteristics can improve the accuracy of energy predictions and identify unnecessary energy consumption. For optimization of thermal comfort, the methodology included detailed simulations (considering all possible sky conditions during the year) and multi-objective optimization (considering indoor visual and thermal comfort as well as visual connectivity to the exterior) and was validated by field measurements to find the optimum for local Sydney homes. The results show that by optimizing the Anidolic daylighting system, daylight ingress can be optimized without the need for expensive active tracking systems, thus achieving a reasonable compromise between visual and thermal comfort and energy consumption. [PERSON] et al. [58] studied specific building networks in seven Chinese cities, i.e., the impact of shading between buildings on the energy demand of buildings, and the use of models and simulations to make predictions and assessments of this impact to assess the impact of shading from surrounding buildings on the heating/cooling energy demand of different community forms. A parametric building model was modelled using Grasshopper's graphical algorithm editor and then multi-threaded simulations were carried out using EnergyPlus. The results show that building shading has a deeper energy impact in summer and a weaker one in winter. The SVR model built in an urban environment allows for more accurate and faster prediction of building heating/cooling energy demand under the influence of shading. In the future, it will become increasingly important to capture the important shading that can influence the accuracy of building energy modelling predictions, particularly in highly urbanized areas. Simulation results from EnergyPlus are used in conjunction with programming tools to compare and select suitable energy systems. This area includes the optimization of energy demand for a multi-story hotel structure in the Tehran region of Iran by [PERSON] et al. [74] Using data from the Tehran region entered into EnergyPlus to calculate hotel energy consumption, the optimization of building parameters using JEPlus + EA software and non-dominated ranking genetic algorithms (NSGA), and economic calculations using Homer Pro software. By selecting the optimal building variables in line with the climatic conditions, the energy demand of the building can be significantly reduced. Furthermore, despite the high cost of renewable energy systems, with the significant reduction in the cost of electricity production, long-term savings can be invested in the installation of photovoltaic solar systems, making the buildings more sustainable. [PERSON] et al. [72] investigated a methodological framework for predicting building energy consumption in light temporary residential buildings built during COVID-19, i.e., the impact of wall design on inherent energy consumption and operational energy consumption. Rapid energy consumption predictions for a light building in Nanjing, China were achieved using BIM, the Eco-invent database and EnergyPlus software. Conclusions were obtained regarding the impact of material thickness and facade materials, and an automated method for calculating energy consumption data using a combination of EnergyPlus, SPSS, and programming software was identified. [PERSON] et al.