BIM, 3D Cadastral Data and AI for Weather Conditions Simulation and Energy Consumption Monitoring

This paper is part of an ongoing research study on developing a methodology for the low-cost creation of the Digital Twin of an urban neighborhood for sustainable, transparent, and participatory urban management to enable low-and middle-income economies to meet the UN Sustainable Development Agenda 2030 successfully and timely, in particular SDGs 1, 7, 9, 10, 11, and 12. The methodology includes: (1) the creation of a geospatial data infrastructure by merging Building Information Models (BIMs) and 3D cadastral data that may support a number of applications (i.e., visualization of 3D volumetric legal entities), and (2) the use of Artificial Intelligence (AI) platforms, Machine Learning (ML), and sensors that are interconnected with devices located in the various property units to test and predict future scenarios and support energy efficiency applications. Two modular platforms are created: (1) to interact with the AI sensors for building tracking and management purposes (i.e., alarms, security cameras, control panels, etc.) and (2) to analyze the energy consumption data such as future predictions, anomaly detection, and scenario making. A case study is made for an urban neighborhood in Athens. It includes a dynamic weather simulation and visualization of different seasons and times of day in combination with internal energy consumption.