Mathematical Problems in Engineering

Volume 2015, Article ID 267974, 12 pages

http://dx.doi.org/10.1155/2015/267974

## Optimal Buildings’ Energy Consumption Calculus through a Distributed Experiment Execution

^{1}Universitat Politècnica de Catalunya-BarcelonaTech, 08034 Barcelona, Spain^{2}Institut de Recerca en Energia de Catalunya (IREC), 08930 Barcelona, Spain

Received 2 June 2015; Revised 31 July 2015; Accepted 20 September 2015

Academic Editor: Fons J. Verbeek

Copyright © 2015 Pau Fonseca i Casas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### 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.

#### 1. Introduction

Environmental simulation is a demanding area for several reasons. First, the models depend on a greater number of variables and factors that usually are higher than in other disciplines. Second, the teams that are involved in the definition and implementation of the models belong to several different areas, implying that a common language is needed to begin working. Third, because of the substantial number of parameters and factors that exist in the models, the experimentation tends to become time and resource consuming. In this paper, a methodology that simplifies the communication between the different actors that are involved in the project is presented. This methodology defines a distributed execution scenario for these models. This distributed scenario execution reduces the time needed to obtain the results and possibly the exploration of more alternatives as we will discuss later.

The paper is organized as follows. In Section 2, we describe the types of systems we want to model. In Section 3, the model is presented, and the selected typologies, results, and factors are analyzed. Section 4 presents the formalization of the model. Section 5 presents the implementation, and Section 6 describes the procedure we follow to distribute the experimentation. Finally, the conclusions are presented in Section 7.

#### 2. The System

The “Energy Performance of Buildings Directive (EPBD),” approved by the 2010/31/EU European directive, aims to speed up the energy saving policies in the building sector to achieve a 20% reduction in energy consumption, to reduce greenhouse gas emissions by 20%, and to increase the use of renewable energy to 20% in the European Union. Among many other measures, Article 9 of the directive stipulates that, by December 31, 2020, the energy consumption of new buildings must be near zero and that, by December 31, 2018, the energy consumption for occupied buildings and/or public property buildings must be near zero. In relation to this measure, the board recommends that the Member States establish intermediate objectives in 2015 and that they gradually adopt these goals until 2020 to ensure compliance with the objectives set.

In relation to the energy renovation of buildings, where the present study is focused, a series of measures must be taken to ensure that the minimum requirements are compiled when renewing at least 25% of the building or its surroundings. The same policy explains that, to adjust and set the minimum requirements for energy efficiency, all methods must be based on a cost-benefit analysis to achieve optimal levels of profitability.

The MARIE project, which is framed in the Catalan context and led by the Department of Territory and Sustainability of Catalonia, has the overall aim of defining a strategy for improving the energy refurbishment of Mediterranean buildings. In this context, the study aims to provide the necessary management and to set the minimum criteria for energy renovation, ensuring optimal levels from an energy and economic point of view and proposing solutions adapted to the particular building characteristics of Catalonia (climate and construction).

Therefore, the overall objective of this project is to conduct a technical study to find optimal values regarding energy consumption. With the knowledge obtained from the simulations, it is possible to propose modifications on the buildings to gradually achieve near-zero energy buildings (NZEB). This type of analysis is becoming more common because the benefits are clear. As an example, [1] analyzed and evaluated the energy saving, bill saving, and payback period and avoided emissions of one large medical center, while [2] analyzed the energy consumption for educational buildings. In these cases similar to our case, the criteria that must be considered consist of a set of variables; hence, the optimization criteria on this project follow a multiobjective schema. In our case, we are not focused on a single building similar to [1, 2]; hence, the number of experiments we will conduct grows exponentially. Therefore, we need to distribute the experimental design; other approaches exist, as presented in [3], where we analyze the relations among variables in the greenhouse by identifying probabilistic dependencies between them with the goal of allowing us to do predictions without the need of observing all of the variables present in the model. Another example is in [4], where we analyze the materials used on the construction in the South European area from the point of view of several environmental and economic indicators. However, with our approach, we can obtain a complete map detailing the behavior for the typologies selected and the interrelations between all of the factors.

This study is focused on four representative typologies and four climates in Catalonia (see Figure 1). A dynamic simulation of every building typology was performed in TRNSYS [5]. One of the most challenging problems for this type of simulation is the considerable number of factors we must consider. The building models include a detailed characterization of the building and their systems and the behavior of the occupants. The results obtained for each simulation are (i) energy consumption, (ii) comfort evaluation, and (iii) global costs calculation.