This page has only limited features, please log in for full access.
Current climate change threats and increasing CO2 emissions, especially from the building stock, represent a context where action is required. It is necessary to provide efficient manners to manage energy demand in buildings and contribute to a decarbonised future. By combining new technologies, such as artificial intelligence, Internet of things, blockchain, and the exploitation of big data towards solving real life problems, the way could be paved towards smart and energy-aware buildings. In this context, the aim of this paper is to present a critical review and an in-detail definition of the big data value chain for the built environment in Europe, covering multiple needs and perspectives: “policy”, “technology” and “business”, in order to explore the main challenges and opportunities in this area.
Gema Hernández-Moral; Sofía Mulero-Palencia; Víctor Serna-González; Carla Rodríguez-Alonso; Roberto Sanz-Jimeno; Vangelis Marinakis; Nikos Dimitropoulos; Zoi Mylona; Daniele Antonucci; Haris Doukas. Big Data Value Chain: Multiple Perspectives for the Built Environment. Energies 2021, 14, 4624 .
AMA StyleGema Hernández-Moral, Sofía Mulero-Palencia, Víctor Serna-González, Carla Rodríguez-Alonso, Roberto Sanz-Jimeno, Vangelis Marinakis, Nikos Dimitropoulos, Zoi Mylona, Daniele Antonucci, Haris Doukas. Big Data Value Chain: Multiple Perspectives for the Built Environment. Energies. 2021; 14 (15):4624.
Chicago/Turabian StyleGema Hernández-Moral; Sofía Mulero-Palencia; Víctor Serna-González; Carla Rodríguez-Alonso; Roberto Sanz-Jimeno; Vangelis Marinakis; Nikos Dimitropoulos; Zoi Mylona; Daniele Antonucci; Haris Doukas. 2021. "Big Data Value Chain: Multiple Perspectives for the Built Environment." Energies 14, no. 15: 4624.
In recent years, new technologies, such as Artificial Intelligence, are emerging to improve decision making based on learning. Their use applied to the Architectural, Engineering and Construction (AEC) sector, together with the increased use of Building Information Modeling (BIM) methodology in all phases of a building’s life cycle, is opening up a wide range of opportunities in the sector. At the same time, the need to reduce CO
Sofía Mulero-Palencia; Sonia Álvarez-Díaz; Manuel Andrés-Chicote. Machine Learning for the Improvement of Deep Renovation Building Projects Using As-Built BIM Models. Sustainability 2021, 13, 6576 .
AMA StyleSofía Mulero-Palencia, Sonia Álvarez-Díaz, Manuel Andrés-Chicote. Machine Learning for the Improvement of Deep Renovation Building Projects Using As-Built BIM Models. Sustainability. 2021; 13 (12):6576.
Chicago/Turabian StyleSofía Mulero-Palencia; Sonia Álvarez-Díaz; Manuel Andrés-Chicote. 2021. "Machine Learning for the Improvement of Deep Renovation Building Projects Using As-Built BIM Models." Sustainability 13, no. 12: 6576.