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Sofía Mulero-Palencia
CARTIF Technology Centre, Parque Tecnológico de Boecillo, Boecillo, 47151 Valladolid, Spain

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Review
Published: 30 July 2021 in Energies
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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.

ACS Style

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 Style

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 (15):4624.

Chicago/Turabian Style

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. 2021. "Big Data Value Chain: Multiple Perspectives for the Built Environment." Energies 14, no. 15: 4624.

Journal article
Published: 09 June 2021 in Sustainability
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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 CO2 emissions in cities is focusing on the energy renovation of existing buildings, thus tackling one of the main causes of these emissions. This paper shows the potentials, constraints and viable solutions of the use of Machine Learning/Artificial Intelligence approaches at the design stage of deep renovation building projects using As-Built BIM models as input to improve the decision-making process towards the uptake of energy efficiency measures. First, existing databases on buildings pathologies have been studied. Second, a Machine Learning based algorithm has been designed as a prototype diagnosis tool. It determines the critical areas to be solved through deep renovation projects by analysing BIM data according to the Industry Foundation Classes (IFC4) standard and proposing the most convenient renovation alternative (based on a catalogue of Energy Conservation Measures). Finally, the proposed diagnosis tool has been applied to a reference test building for different locations. The comparison shows how significant differences appear in the results depending on the situation of the building and the regulatory requirements to which it must be subjected.

ACS Style

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 Style

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 (12):6576.

Chicago/Turabian Style

Sofía 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.