Optimal buildings’ energy consumption calculus through a distributed experiment execution
The calculus of building energy consumption is a demanding task because multiple factors must be considered during experimentation. Additionally, the definition of the model and the experiments is complex because the problem is multidisciplinary. When we face complex models and experiments that requ...
| Autores: | , , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/78876 |
| Acesso em linha: | https://hdl.handle.net/2117/78876 https://dx.doi.org/10.1155/2015/267974 |
| Access Level: | acceso abierto |
| Palavra-chave: | Operations research Investigació operativa Classificació AMS::90 Operations research, mathematical programming::90B Operations research and management science Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Optimització |
| Resumo: | The calculus of building energy consumption is a demanding task because multiple factors must be considered during experimentation. Additionally, the definition of the model and the experiments is complex because the problem is multidisciplinary. When we face complex models and experiments that require a considerable amount of computational resources, the application of solutions is imperative to reduce the amount of time needed to define the model and the experiments and to obtain the answers. In this paper, we first address the definition and the implementation of an environmental model that describes the behavior of a building from a sustainability point of view and enables the use of several simulations and calculus engines in a cosimulation scenario. Second, we define a distributed experimental framework that enables us to obtain results in an accurate amount of time. This methodology has been applied to the energy consumption calculation, but it can also be applied to other modeling problems that usually require a considerable amount of resources by reducing the amount of time needed to perform modeling, implementation, verification, and experimentation. |
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