State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

How to pinpoint energy-inefficient Buildings? An Approach based on the 3D City model of Vienna

This paper describes a methodology to assess the energy performance of residential buildings starting from the semantic 3D city model of Vienna. Space heating, domestic hot water and electricity demand are taken into account. The paper deals with aspects related to urban data modelling, with particular attention to the energy-related topics, and with issues related to interactive data exploration/visualisation and management from a plugin-free web-browser, e.g. based on Cesium, a WebGL virtual globe and map engine. While providing references to existing previous works, only some general and introductory information is given about the data collection, harmonisation and integration process necessary to create the CityGML-based 3D city model, which serves as the central information hub for the different applications developed and described more in detail in this paper. The work aims, among the rest, at developing urban decision making and operational optimisation software tools to minimise non-renewable energy use in cities. The results obtained so far, as well as some comments about their quality and limitations, are presented, together with the discussion regarding the next steps and some planned improvements

Smart City Digital Twin Framework for Real-Time Multi-Data Integration and Wide Public Distribution

Digital Twins are digital replica of real entities and are becoming fundamental tools to monitor and control the status of entities, predict their future evolutions, and simulate alternative scenarios to understand the impact of changes. Thanks to the large deployment of sensors, with the increasing information it is possible to build accurate reproductions of urban environments including structural data and real-time information. Such solutions help city councils and decision makers to face challenges in urban development and improve the citizen quality of life, by ana-lysing the actual conditions, evaluating in advance through simulations and what-if analysis the outcomes of infrastructural or political chang-es, or predicting the effects of humans and/or of natural events. Snap4City Smart City Digital Twin framework is capable to respond to the requirements identified in the literature and by the international forums. Differently from other solutions, the proposed architecture provides an integrated solution for data gathering, indexing, computing and information distribution offered by the Snap4City IoT platform, therefore realizing a continuously updated Digital Twin. 3D building models, road networks, IoT devices, WoT Entities, point of interests, routes, paths, etc., as well as results from data analytical processes for traffic density reconstruction, pollutant dispersion, predictions of any kind, what-if analysis, etc., are all integrated into an accessible web interface, to support the citizens participation in the city decision processes. What-If analysis to let the user performs simulations and observe possible outcomes. As case of study, the Digital Twin of the city of Florence (Italy) is presented. Snap4City platform, is released as open-source, and made available through GitHub and as docker compose

Dense 3D Object Reconstruction from a Single Depth View

In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high resolution of 256^3 by recovering the occluded/missing regions. The key idea is to combine the generative capabilities of autoencoders and the conditional Generative Adversarial Networks (GAN) framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets and real-world Kinect datasets show that the proposed 3D-RecGAN++ significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects.Comment: TPAMI 2018. Code and data are available at: https://github.com/Yang7879/3D-RecGAN-extended. This article extends from arXiv:1708.0796

Heritage-led ontologies: Digital platform for supporting the regeneration of cultural and historical sites

The increasing application of digital technologies to cultural heritage (CH) is wide and well documented, including a variety of tools such as digital archives, online guides and HBIM repositories. Several vocabularies and ontologies were designed to order heritage data and make CH more accessible and exploitable. However, these tools have often focused on a particular dimension of CH producing high value in separate sectors (e.g. access to conservation of historic buildings and data valorisation for restoration of heritage assets) but lacking ways for adapting or replicating the model to urban complex systems. Moreover, many studies and tools show large effort in cataloguing and archiving, but less in providing tools for designing and managing. The ROCK platform, developed within the Horizon 2020 (H2020) funded project ROCK (GA 730280), addresses the need for a management and interventionoriented interoperable tool, aimed at storing, visualizing, elaborating and linking data on cultural heritage. The use of already existing ontologies was not sufficient for developing a tool to deal with the complexity of urban systems and heterogeneous data sources. Instead, a participative methodology was set in place for the development of a context-based semantic framework to define the needs and requirements of heritage-led regeneration actions