Office representatives for cost-optimal energy retrofitting analysis: A novel approach using cluster analysis of energy performance certificate databases

Gangolells, Marta; Casals, Miquel; Ferré-Bigorra, Jaume; Forcada, Núria; Macarulla, Marcel; Gaspar, Kàtia; Tejedor, Blanca

doi:10.1016/j.enbuild.2019.109557

Cited by 27 publications

(15 citation statements)

References 20 publications

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“…Regarding the first question (RQ1), 13 studies used the EPC dataset [30,[32][33][34][35]52,58] as explained in Sections 4.4 and 4.5. This kind of data is multi-dimensional, given that each energy certificate has many attributes.…”

Section: Research Questions Discussionmentioning

confidence: 99%

“…For De Stasio Claudio with a link strength of 9 [23][24][25] and De Masi Rosa Francesca with a link strength of 8 [26,27] (Table 4), Cluster 2 (red) has seven items and found Ali Usman and Bohacek Mark. For Hoare Cathal, Mangina Eleni, O'Donnell James, Purcell Karl, Shamsi Mohammad Haris [28][29][30], Cluster 3 (yellow) has four items and found Casals Miquel, Ferré-Bigorra Jaume, Gangolells Marta, and Macarulla Marcel [31,32]. Cluster 4 (blue) has three items and found Capozzoli Alfonso, Cerquitelli Tania and Piscitelli Marco Savino [33][34][35], and Cluster 5 (cyan) has three items and found Ciancio Virgilio, Dell'olmo Jacopo and Salata Ferdinando [36,37].…”

Section: Authors' Co-authorship Analysismentioning

confidence: 99%

“…Some of them combine different types of buildings. Five papers combined residential and commercial buildings-offices and schools [25,32,44,52,58], two papers addressed offices [31,49] and six analyzed schools and universities [27,38,40,50,51,59]. Only one addressed a hospital [23] and one a care home [48].…”

Section: Most Cited Publicationsmentioning

confidence: 99%

“…Few studies use only ML methods, namely ( 13) studies [28,29,31,[33][34][35]40,45,48,50,52,56,59]. The most common clustering and classification techniques were K-means (7), statistical methods (6), Latin hypercube sampling (2), other manual groupings (2), decision tree (2), and probability density function (1) [23,25,[28][29][30][31][32][33][34]47,[50][51][52]54,55,57,59].…”

Section: Most Cited Publicationsmentioning

confidence: 99%

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Machine Learning Techniques Focusing on the Energy Performance of Buildings: A Dimensions and Methods Analysis

2021

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The problem of energy consumption and the importance of improving existing buildings’ energy performance are notorious. This work aims to contribute to this improvement by identifying the latest and most appropriate machine learning or statistical techniques, which analyze this problem by looking at large quantities of building energy performance certification data and other data sources. PRISMA, a well-established systematic literature review and meta-analysis method, was used to detect specific factors that influence the energy performance of buildings, resulting in an analysis of 35 papers published between 2016 and April 2021, creating a baseline for further inquiry. Through this systematic literature review and bibliometric analysis, machine learning and statistical approaches primarily based on building energy certification data were identified and analyzed in two groups: (1) automatic evaluation of buildings’ energy performance and, (2) prediction of energy-efficient retrofit measures. The main contribution of our study is a conceptual and theoretical framework applicable in the analysis of the energy performance of buildings with intelligent computational methods. With our framework, the reader can understand which approaches are most used and more appropriate for analyzing the energy performance of different types of buildings, discussing the dimensions that are better used in such approaches.

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Section: Research Questions Discussionmentioning

confidence: 99%

Section: Authors' Co-authorship Analysismentioning

confidence: 99%

Section: Most Cited Publicationsmentioning

confidence: 99%

Section: Most Cited Publicationsmentioning

confidence: 99%

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Machine Learning Techniques Focusing on the Energy Performance of Buildings: A Dimensions and Methods Analysis

2021

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“…Improving buildings' energy performance by roof refurbishment was calculated with the CE3X energy certification software. This official software is approved by the Spanish Ecological Transition Energy Ministry [20][21][22], available at: https://energia.gob.es/desarrollo/EficienciaEnergetica/CertificacionEnergetica/DocumentosReconocidos/ Paginas/procedimientos-certificacion-proyecto-terminados.aspx (last accessed 5 June 2020). Those buildings that statistically represented the whole building stock were simulated.…”

Section: Methodsmentioning

confidence: 99%

Contribution of Roof Refurbishment to Urban Sustainability

et al. 2020

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Achieving sustainable urban environments is a challenging goal—especially in existing cities with high percentages of old and obsolete buildings. This work analyzes the contribution of roof refurbishment to sustainability, considering that most roofs are currently underused. Many potential benefits of refurbishment can be achieved, such as the improvement of the energy performance of the buildings and the use of a wasted space for increasing green areas or for social purposes. In order to estimate the degree of the improvement, a vulnerable area in Castellón (east Spain) was selected as a case study. A thorough analysis of the residential building stock was undertaken. Using georeferenced information from the Cadastral Office we classified them according to typology, year of construction and roof type. Some refurbishment solutions were proposed and their applicability to the actual buildings was analyzed under different criteria. The theoretical benefits obtained in the neighborhood such as energy and carbon emissions savings were evaluated, together with the increase of green areas. Moreover, other social uses were suggested for neglected urban spaces in the area. Finally, a more accurate analysis was performed combining different solutions in a specific building, according to its particular characteristics.

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Review on Building Energy Performance Labeling: Whole Life-Cycle Perspective

Yi¹,

Wang²

2021

Proceedings of the 25th International Symposium on Advancement of Construction Management and Real Estate

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Office representatives for cost-optimal energy retrofitting analysis: A novel approach using cluster analysis of energy performance certificate databases

Cited by 27 publications

References 20 publications

Machine Learning Techniques Focusing on the Energy Performance of Buildings: A Dimensions and Methods Analysis

Machine Learning Techniques Focusing on the Energy Performance of Buildings: A Dimensions and Methods Analysis

Contribution of Roof Refurbishment to Urban Sustainability

Review on Building Energy Performance Labeling: Whole Life-Cycle Perspective

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