Uncertainty and sensitivity analysis in building simulation: A probabilistic approach to the real estate market of apartments in Santiago de Chile

Autor: Encinas Pino, Felipe, Sanchez de la Flor, Francisco José, Aguirre Núñez, Carlos, Álvarez Domínguez, Servando, PLEA 2011 - 27th International Conference on Passive and Low Energy Architecture: Architecture & Sustainable Development
Přispěvatelé: UCL - SST/ILOC - Faculté d'Architecture, d'Ingénierie architecturale, d'Urbanisme, Universidad de Cádiz - Escuela Superior de Ingeniería, Universidad Politécnica de Cataluña - Centro de Política de Suelo y Valoraciones, Universidad de Sevilla - Escuela Superior de Ingenieros, DIE – Grupo de Termotecnia
Jazyk: angličtina
Rok vydání: 2011
Předmět:
Popis: It is clear that the thermophysical properties of materials, occupancy patterns and internal gains represent some of the most important sources of uncertainty in the field of building simulation. Uncertainty and sensitivity analysis deals with this situation, since it can generate a great range of forecast values based on the distribution of the input variables. At the same time, these techniques allow to determine as each variable contribute to the total variance of output results. However, most of the building energy simulation programs are deterministic, rather than probabilistic and consequently their results frequently are not expressed in terms of probabilities. On the contrary, the probabilistic approach requires a more complex process, since parameters quantification requires not only an assessment of the point estimate, but also an assessment of the uncertainty. This research aims to define the uncertainty of the predicted energy performance by means of the comparison between factorial design and Monte Carlo Analysis for a sample of 7776 cases that belong to the real estate market of apartments in Santiago de Chile. A total of 9 input parameters constitute the basis for these analyses using the standard EN ISO 13790 as calculation algorithm for estimating the annual heating demand.
Databáze: OpenAIRE