Optimal Buildings’ Energy Consumption Calculus through a Distributed Experiment Execution

Abstract

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.

Using Specification and Description Language for Life Cycle Assesment in Buildings

“…These optimization techniques allow the dramatic reduction of the number of scenarios to be executed, whilst still obtaining a good answer. The time needed to obtain an answer for an experimental design can be large, as suggested in [46] who presented an example of an execution of a real experiment in a distributed scenario. The criteria used was based on an expression that combined the different variables that were interesting in the definition of the LCA.…”

Specification and Description Language Models Automatic Execution in a High-Performance Environment

“…An example of this is the simulation of the current pandemic situation in Catalonia, see [50], where the modeling using SDL largely simplifies the interaction between the different actors (specialists) that are involved in the project. Other examples where this approach has been used are in the construction sector [28,44], where the model connects with the energyPlus [51] engine to optimize building sustainability for the simulation of slap avalanches [43], software [52][53][54], or the simulation of industrial systems [38]. The capability to generate the code that can later be executed on Marenostrum allows us to avoid the constraint related to the computational capabilities of the infrastructure we will use.…”