Abstract
Increasing the energy efficiency of buildings is a strategic objective in the European Union, and it is the main reason why numerous studies have been carried out to evaluate and reduce energy consumption in the residential sector. The process of evaluation and qualification of the energy efficiency in existing buildings should contain an analysis of the thermal behavior of the building envelope. To determine this thermal behavior and its representative parameters, we usually have to use destructive auscultation techniques in order to determine the composition of the different layers of the envelope. In this work, we present a nondestructive, fast, and cheap technique based on artificial neural network (ANN) models that predict the energy performance of a house, given some of its characteristics. The models were created using a dataset of buildings of different typologies and uses, located in the northern area of Spain. In this dataset, the models are able to predict the U-opaque value of a building with a correlation coefficient of 0.967 with the real U-opaque measured value for the same building.
Highlights
Is sector is subject to numerous initiatives of different administrations, all aimed at reducing CO2 emissions and energy consumption, especially in the housing stock of buildings built before 2006, the year when the Technical Building Code regulation in Spain started. e Basic Document of Energy Saving established the requirements that buildings and their thermal systems must comply with [4]. ese regulations and requirements were the reason for the creation of a set of rules [5] to evaluate the energy efficiency of buildings
We put some ctitious values in the dataset trying to simulate all the possible inputs that the artificial neural network (ANN) may have in the variable range and we evaluate the network in this variable grid
ANNs are a good model for predicting the U-Opaque of a building using several of its characteristics. e outputs of the ANNs trained in our dataset have a high correlation coefficient with the real U-Opaque measured values for the same buildings
Summary
E Basic Document of Energy Saving established the requirements that buildings and their thermal systems must comply with [4]. Ese regulations and requirements were the reason for the creation of a set of rules [5] to evaluate the energy efficiency of buildings. In order to meet the requirements established by the regulation, we have to first evaluate the building energy efficiency and, if necessary, apply energy saving measures. (ii) Active measures, whose objective is to reduce the nonrenewable sources energy consumption, Advances in Civil Engineering increasing the performance of thermal equipment related to the conditioning of the indoor environment. Any technique created to evaluate building energy efficiency has to include some criteria to facilitate the acquisition of knowledge about the enclosure thermal behavior and the thermal equipment [6]. It needs to create a model, from datasets (databases) of evaluations in similar buildings [7], which provides a method for estimating the results of an evaluation. ere are several approaches for creating the estimation method [8], all of which can be classified into three general categories [9]:
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