Modeling of Energy Efficiency for Residential Buildings Using Artificial Neuronal Networks

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.

On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings

“…The application of machine learning has emerged as a powerful tool in building energy management (Bui et al, 2019). For example, Alvarez et al (2018) predicted the energy performance of a house using artificial neural network (ANN) models. The models were developed using a dataset of different buildings located in Spain.…”