The article "From White to Black-Box Models: A Review of Simulation Tools for Building Energy Management and Their Application in Consulting Practices" by Amir Shahcheraghian, Hatef Madani, and Adrian Ilinca provides a comprehensive review of simulation tools used in building energy management. The authors categorize these tools into white-box and black-box models based on the level of detail and transparency in their inputs and outputs. White-box models, rooted in fundamental physics and engineering principles, offer detailed simulations of building energy systems, while black-box models use empirical data and machine learning algorithms to predict energy consumption and behavior. The review highlights the importance of these tools in assessing and optimizing building energy performance, emphasizing their integration into consulting practices. It discusses the advantages and limitations of each model type, such as the detailed understanding provided by white-box models and the rapid results offered by black-box models. The authors also explore the scalability and adaptability of these tools across different project scales, from single-family homes to urban districts. Key tools discussed include EnergyPlus, TRNSYS, CitySim, and IDA-ICE for white-box models, and Support Vector Machines, Random Forests, and Deep Neural Networks for black-box models. Each tool's features, applications, and specific strengths are detailed, providing insights into their utility in various consulting scenarios. The review aims to guide researchers, building designers, and engineers in selecting and using the most appropriate simulation tools for their projects, ultimately contributing to more efficient and sustainable building energy management.The article "From White to Black-Box Models: A Review of Simulation Tools for Building Energy Management and Their Application in Consulting Practices" by Amir Shahcheraghian, Hatef Madani, and Adrian Ilinca provides a comprehensive review of simulation tools used in building energy management. The authors categorize these tools into white-box and black-box models based on the level of detail and transparency in their inputs and outputs. White-box models, rooted in fundamental physics and engineering principles, offer detailed simulations of building energy systems, while black-box models use empirical data and machine learning algorithms to predict energy consumption and behavior. The review highlights the importance of these tools in assessing and optimizing building energy performance, emphasizing their integration into consulting practices. It discusses the advantages and limitations of each model type, such as the detailed understanding provided by white-box models and the rapid results offered by black-box models. The authors also explore the scalability and adaptability of these tools across different project scales, from single-family homes to urban districts. Key tools discussed include EnergyPlus, TRNSYS, CitySim, and IDA-ICE for white-box models, and Support Vector Machines, Random Forests, and Deep Neural Networks for black-box models. Each tool's features, applications, and specific strengths are detailed, providing insights into their utility in various consulting scenarios. The review aims to guide researchers, building designers, and engineers in selecting and using the most appropriate simulation tools for their projects, ultimately contributing to more efficient and sustainable building energy management.