# Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy
## 6 Challenges and Future Directions
### BEM Algorithms  
We are in the early stages of exploring machine learning applications for energy simulation and are facing several challenges, mainly in data and model selection, prediction accuracy, interpretability, computational complexity, and interdisciplinary collaboration. Data is the foundation of machine learning models; thus, it is quality and availability that directly impact model performance [174,188]. For example, on the one hand, lack of detailed data and poor data accuracy can affect the accuracy of prediction results [151]. On the other hand, to accurately predict energy use, models require a large amount of historical data [151,182], which may only sometimes be available, especially for resource-poor areas [177]. In addition, the absence and handling of data, and the lack of a specific energy code and standard to refer to, are also current issues [109]. Regarding model selection and prediction accuracy, appropriate features and construction are essential for the accurate estimation of machine learning models for the energy use [171] and how to select the suitable machine learning algorithms and parameters for a given dataset and problem. How to select suitable machine learning algorithms and parameters for a given dataset and problem. In the case of building energy simulation, the diversity of building characteristics (e.g., geographical location, building type, age of the building, etc.) can also affect the performance of the model [175,182], making the computational accuracy of the model a challenge. In addition, machine learning models' interpretability and computational complexity are very important issues. The decision process and results of the model may be more brutal to understand [94,174,189]. Challenges in this area also include how to deal with and understand the impact of human behavior on building energy prediction, a complex issue requiring more research to address [172,180]. The computational time required to run simulations with large areas and high-resolution data can be a significant drawback for the computational complexity [176]. Furthermore, optimizing, and tuning models require significant computational power, while manual modelling requires expert knowledge and significant working time [125]. Furthermore, interdisciplinary collaboration and integrated analysis are critical to successfully addressing the above challenges. For example, building energy efficiency requires consideration of multiple factors such as building design parameters, weather conditions, equipment systems, and occupant behaviors [180; 181], and this requires interdisciplinary collaboration and integrated analysis [177]. In addition, climate change will exacerbate the challenges of modelling and forecasting building energy use, and more research and practice is needed to refine and validate methods for this emerging field [177]. Based on the literature covered in this survey, several possible future research directions exist for applying machine learning in building energy simulation. The first is that the generalization capability and accuracy of the models can be explored in depth. Models need to be continuously validated and improved to increase their reliability [172; 173]. For example, the model's performance can be improved by integrating more industrial data to model complex non-linear relationships [171]. Also, the scope of application of the models needs to be further expanded, and these ML models will be expected to be applied to a broader range of building types and climatic conditions, as well as to more prominent scale buildings [178]. In terms of data, massive datasets are needed for model training and testing [173], while data quality and availability are also expected to improve [174]. Future research could also focus more on empirical data such as building samples, occupancy rates, and building use patterns to support the application of models [182].