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About this book

This book is about the optimization of the characterization of the thermal properties of building envelopes, through experimental tests and the use of artificial intelligence. It analyses periodic and stationary thermal properties using measurement approaches based on the heat flow meter method and the thermometric method. These measurements are then analysed using advanced artificial intelligence algorithms.

The book is structured in four parts, beginning with a discussion of the importance of thermal properties in the energy performance of buildings. Secondly, theoretical and experimental methods for characterizing thermal properties are analysed. Then, the methodology is developed, and the characteristics and properties of the algorithms used are explored. Finally, the results obtained with the algorithms are analysed and the most appropriate approaches are determined.

This book is of interest to researchers, civil and industrial engineers, energy auditors and architects, by providing a resource which improves energy audit tasks in existing buildings.

Table of Contents

Frontmatter

Chapter 1. The Influence of the Envelope Thermal Properties on Building Energy Performance

Abstract
From ancient times, human beings have taken advantage of natural resources as much as possible for their own benefit, although that advantage is limited in order to guarantee the environment sustainability.
David Bienvenido-Huertas, Carlos Rubio-Bellido

Chapter 2. Methods to Assess the Thermal Properties of the Building Envelope

Abstract
The thermal properties of the building envelope are crucial in building energy performance. The variation of envelope thermal values (e.g. through the regulation of a country) directly influences building energy performance.
David Bienvenido-Huertas, Carlos Rubio-Bellido

Chapter 3. Methodological Framework of Artificial Intelligence Algorithms and Generation of the Dataset

Abstract
The analysis of the state-of-the-art methods to characterize thermal properties has shown the importance of the theoretical methods (both of stationary and periodic properties) and the difficulty to characterize the existing buildings correctly.
David Bienvenido-Huertas, Carlos Rubio-Bellido

Chapter 4. Estimation of Stationary Thermal Properties with Artificial Intelligence

Abstract
This chapter aims to develop a mathematical model by using MLP and RF to estimate the thermal transmittance of ISO 6946 (\({U}_{6946}\)) through in situ measured variables. For this purpose, the scheme of variables of HFM and THM is used.
David Bienvenido-Huertas, Carlos Rubio-Bellido

Chapter 5. Estimating Periodic Thermal Properties with Artificial Intelligence

Abstract
This chapter aims to develop a mathematical model through MLP and RF to estimate the periodic thermal properties of ISO 13786 through in situ measured variables.
David Bienvenido-Huertas, Carlos Rubio-Bellido

Chapter 6. Analysing with Artificial Intelligence Other Approaches to Experimental Thermal Characterization in the Existing Buildings

Abstract
This chapter aims to develop mathematical models based on MLP and RF that optimize the experimental methods to characterize the thermal transmittance. In particular, the models developed address the possibility of eliminating the error related to the theoretical formulation of THM.
David Bienvenido-Huertas, Carlos Rubio-Bellido
Additional information