Este archivo ha sido creado el 13/05/2022 por Carmen María Calama González. GENERAL INFORMATION ------------------ 1. Dataset title: Medidas de rehabilitación optimizadas aplicadas al parque residencial social del sur de España en diferentes zonas climáticas, ante escenarios climáticos actuales. Visualización de resultados de forma interactiva [Dataset]. Optimal retrofit solutions of the social housing stock of southern Spain for different climatic areas, under current weather conditions. Visualization of interactive results [Dataset]. 2. Authorship: Name: Carmen María Calama González Institution: Instituto Universitario de Arquitectura y Ciencias de la Construcción. Universidad de Sevilla. Email: ccalama@us.es ORCID: 0000-0002-6511-2885 Name: Ángel Luis León Rodríguez Institution: Instituto Universitario de Arquitectura y Ciencias de la Construcción. Universidad de Sevilla. Email: leonr@us.es ORCID: 0000-0003-3466-7850 Name: Rafael Suárez Medina Institution: Instituto Universitario de Arquitectura y Ciencias de la Construcción. Universidad de Sevilla. Email: rsuarez@us.es ORCID: 0000-0001-6136-1596 DESCRIPTION ---------- 1. Dataset language: Inglés 2. Abstract: The interactive research results presented were reported in the following project: Optimización Paramétrica de Fachadas de Doble Piel en Clima Mediterráneo para la Mejora de la Eficiencia Energética ante Escenarios de cambio Climático (BIA2017-86383-R). Specifically, optimal retrofit solutions applied to the social housing stock of southern Spain (Mediterranean climate), under current weather climate conditions, are presented. Several climatic areas of southern Spain are assessed: A3, A4, B4, C3. To do so, a multi-objective decision analysis has been conducted through the application of genetic algorithms in order to optimise retrofit solutions considering the following aspects: minimising indoor thermal discomfort during summer (in terms of analysing the annual percentage of overheating hours) and winter (analysing the annual percentage of undercooling hours), as well as investment costs of retrofit solutions (expressed in € per built m2). In this process, paramterized building dynamic simulation models, previously validated, representative of the social housing stock (building archectypes of the whole building stock - urban level) have been used. Building characterisation information included in a public database provided by AVRA (Agencia de Vivienda y Rehabilitación de Andalucía) have been implemented into the building stock models through bottom-up approaches. 3. Keywords: Retrofit solutions; social housing stock; Mediterranean climate; numerical optimization; thermal comfort; investment costs; soluciones de rehabilitación; viviendas sociales; clima Mediterráneo; optimización numérica; confort térmico; costes económicos. 4. Date of data collection (fecha única o rango de fechas): 01-01-2020 a 31-12-2020 5. Publication Date: [ Obligatorio si es aplicable. Fecha de depósito en el repositorio | Formato DD-MM-YYYY] 6. Grant information: Grant Agency: Ministerio de Economía y Competitividad Grant Number: BIA2017-86383-R 7. Geographical location/s of data collection: Sur de España (Clima Mediterráneo). Zonas climáticas: A3, A4, B4 y C3. ACCESS INFORMATION ------------------------ 1. Creative Commons License of the dataset: Licencia CC-BY 2. Dataset DOI: https://doi.org/10.1016/j.enbuild.2022.111915 3. Related publication: Calama-González, C. M., Symonds, P., León-Rodríguez, Á. L., & Suárez, R. (2022). Optimal retrofit solutions considering thermal comfort and intervention costs for the Mediterranean social housing stock. Energy and Buildings, 259, 111915, https://doi.org/10.1016/j.enbuild.2022.111915 VERSIONING AND PROVENANCE --------------- 1. Last modification date: 31-01-2022 2. Were data derived from another source?: No METHODOLOGICAL INFORMATION ----------------------- 1. Description of the methods used to collect and generate the data: Energy results have been predicted thorugh the EnergyPlus building dynamic simulation software. Optimization results have been obtained using the jEPlus+EA and implementing a NSGA-II evolutionary algorithmn. 2. Data processing methods: The interactive files have been developed through Python code. 3. Software or instruments needed to interpret the data: Any web browser. FILE OVERVIEW ---------------------- 1. Explain the file naming conversion, si es aplicable: Leter and number refer to the climatic area of southern Spain analysed (A3, A4, B4 and C3), followed by the type of figure: Disperion refers to a dispersion plot, while parallel refers to a parallel coordinates plot 2. File list: File name: A3Dispersion.html Description: 3d dispersion figure of A3 climatic area. Results obtained in the optimisation process: the percentage of annual overheating hours (%), annual undercooling hours (%) and investment costs (€/m2, considering the built area) are represented. Red dots indicate optimal results. For a more detail description of the variables and discussion of results, refer to the publication related to this Dataset (doi). File name: A4Dispersion.html Description: 3d dispersion figure of A4 climatic area. Results obtained in the optimisation process: the percentage of annual overheating hours (%), annual undercooling hours (%) and investment costs (€/m2, considering the built area) are represented. Red dots indicate optimal results. For a more detail description of the variables and discussion of results, refer to the publication related to this Dataset (doi). File name: B4Dispersion.html Description: 3d dispersion figure of B4 climatic area. Results obtained in the optimisation process: the percentage of annual overheating hours (%), annual undercooling hours (%) and investment costs (€/m2, considering the built area) are represented. Red dots indicate optimal results. For a more detail description of the variables and discussion of results, refer to the publication related to this Dataset (doi). File name: C3Dispersion.html Description: 3d dispersion figure of C3 climatic area. Results obtained in the optimisation process: the percentage of annual overheating hours (%), annual undercooling hours (%) and investment costs (€/m2, considering the built area) are represented. Red dots indicate optimal results. For a more detail description of the variables and discussion of results, refer to the publication related to this Dataset (doi). File name: A3Parallel.html Description: Parallel coordinates plot of A3 Climatic area. Optimization parameter combination obtained in the multi-objective decision analysis. The combinatorial variables refer to: natural ventilation schedule (NatVent), mechanical ventilation schedule (MechBent), window blinds aperture schedule (Blinds), type of window glazing (Window glazing) and frame (Window frame), type of retrofit facade solution (Wall) and type of retrofit roof solution (Roof). Results reported are: percentage of annual overheating hours (%), annual undercooling hours (%) and investment costs (€/m2, considering the built area). Orange lines indicate optimal results. For a more detail description of the variables and discussion of results, refer to the publication related to this Dataset (doi). File name: A4Parallel.html Description: Parallel coordinates plot of A4 Climatic area. Optimization parameter combination obtained in the multi-objective decision analysis. The combinatorial variables refer to: natural ventilation schedule (NatVent), mechanical ventilation schedule (MechBent), window blinds aperture schedule (Blinds), type of window glazing (Window glazing) and frame (Window frame), type of retrofit facade solution (Wall) and type of retrofit roof solution (Roof). Results reported are: percentage of annual overheating hours (%), annual undercooling hours (%) and investment costs (€/m2, considering the built area). Orange lines indicate optimal results. For a more detail description of the variables and discussion of results, refer to the publication related to this Dataset (doi). File name: B4Parallel.html Description: Parallel coordinates plot of B4 Climatic area. Optimization parameter combination obtained in the multi-objective decision analysis. The combinatorial variables refer to: natural ventilation schedule (NatVent), mechanical ventilation schedule (MechBent), window blinds aperture schedule (Blinds), type of window glazing (Window glazing) and frame (Window frame), type of retrofit facade solution (Wall) and type of retrofit roof solution (Roof). Results reported are: percentage of annual overheating hours (%), annual undercooling hours (%) and investment costs (€/m2, considering the built area). Orange lines indicate optimal results. For a more detail description of the variables and discussion of results, refer to the publication related to this Dataset (doi). File name: C3Parallel.html Description: Parallel coordinates plot of C3 Climatic area. Optimization parameter combination obtained in the multi-objective decision analysis. The combinatorial variables refer to: natural ventilation schedule (NatVent), mechanical ventilation schedule (MechBent), window blinds aperture schedule (Blinds), type of window glazing (Window glazing) and frame (Window frame), type of retrofit facade solution (Wall) and type of retrofit roof solution (Roof). Results reported are: percentage of annual overheating hours (%), annual undercooling hours (%) and investment costs (€/m2, considering the built area). Orange lines indicate optimal results. For a more detail description of the variables and discussion of results, refer to the publication related to this Dataset (doi). 3. Relationship between files: A file per each climatic area and type of figure has been created. 4. File format: .html 5. If the dataset includes multiple files, specify the directory structure and relationships between the files: The dataset includes 8 .html files: a dispersion figure and a parallel coordinates plot per each climatic area analysed. Results may be visualized independetly. Nonetheless, it is recommended that both the dispersion and parallel plot are simultaneosluy visualized per each climatic area. MORE INFORMATION -------------- Research results presented may be easily accessed through any web browser, using the .html interactive files included. There are two types of figures: dispersion plots and parallel coordinates plots, which have been generated per each climatic area analysed (A3, A4, B4 and C3). In the case of 3d dispersion plots, the figure can be rotated and zoomed, to guarantee an adquate visualization in a 3d space. Placing the mouse on any data point, the related results are showns (thermal comfort results and investment costs). In relation to the parallel coordinates figures, optimization and combinatorial variables may be filtered, selecting and placing the mouse over the X-axis. Then, the web figure will show the combination of the retrofit packages implemented in each case (constructive variables and operational retrofit aspects). Each combination will report an specific value of annual overheating and undercooling hours, as well as an investment cost. Thus, with this tool, it is possible to carried out a quick and general comparative analysis of different retrofit scenarios and select, assessing the results, which retrofit strategies may be more adquate, considering public retrofit programmes and interests.