Abstract
The collective concern for global warming is changing the way we behave and we act in our daily life in order to reduce energy consumption and our CO2 footprint. However, since healthier and more comfortable environments are needed, the best solution is a well-balanced relationship between these two issues. The present article exposes a methodology for a multiobjective analysis which takes into account the consumption and energy efficiency as well as the thermal comfort, based on a field study in non-residential buildings and the application of fuzzy logic. As a result, an improvement related to energy saving and lower costs for buildings’ owners without detrimental to occupants’ thermal comfort and productivity is expected.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Aparicio-Ruiz P, Fernández J, Onieva L (2010) Sistema experto basado en la lógica difusa para la detección de configuraciones climáticas asociadas al confort. In: 4th international conference on industrial engineering and industrial management. Donostia-San Sebastián, pp. 545–554
Barbadilla-Martín E, Salmerón Lissén J, Guadix Martín J, Aparicio-Ruiz P, Brotas L (2017) Field study on adaptive thermal comfort in mixed mode office buildings in southwestern area of Spain. Build Environ 123:163–175. https://doi.org/10.1016/j.buildenv.2017.06.042. ISSN 03601323
Castilla M, Álvarez J, Berenguel M, Pérez M, Rodríguez F, Guzmán J (2010) Técnicas de Control del Confort en Edificios. Revista Iberoamericana de Automática e Informática Industrial RIAI 7(3):5–24. ISSN 16977912. https://doi.org/10.1016/s1697-7912(10)70038-8
De Dear R, Akimoto T, Arens EA, Brager G, Candido C, Cheong K, Li B, Nishihara N, Sekhar S, Tanabe S, Toftum J, Zhang H, Zhu Y (2013) Progress in thermal comfort research over the last twenty years. Indoor Air 23(6):442–461. ISSN 09056947. https://doi.org/10.1111/ina.12046
De Dear RJ, Brager GS, Reardon J, Nicol F (1998) Developing an adaptive model of thermal comfort and preference/discussion. ASHRAE Trans 104:145
Directiva 2012/27/UE del parlamento Europeo y del Consejo (2012) Directiva 2012/27/UE del Parlamento Europeo y del Consejo, pp 1–56
Djongyang N, Tchinda R, Njomo D (2010) Thermal comfort: a review paper. Renew Sustain Energy Rev 14:2626–2640. ISSN 13640321. https://doi.org/10.1016/j.rser.2010.07.040
Dounis A, Caraiscos C (2007) Intelligent coordinator of fuzzy controller agents for indoor environment control in buildings using 3-D fuzzy comfort set. In: IEEE international fuzzy systems conferences, London
Fanger PO (1972) Thermal comfort analysis and applications in environment engineering
Hagras H, Callaghan V, Colley M, Clarke G (2003) A hierarchical fuzzy–genetic multi-agent architecture for intelligent buildings online learning, adaptation and control. Inf Sci 150(1–2):33–57
Kolokotsa D, Stavrakakis G, Kalaitzakis K, Agoris D (2002) Genetic algorithms optimized fuzzy controller for the indoor environmental management in buildings implemented using PLC and local operating networks. Eng Appl Artif Intell 15(5):417–428. https://doi.org/10.1016/s0952-1976(02)00090-8. ISSN 09521976
Mccartney KJ, Nicol FJ (2002) Developing an adaptive control algorithm for Europe. Energy Build 34(6):623–635. https://doi.org/10.1016/s0378-7788(02)00013-0. ISSN 03787788
Rutishauser U, Joller J, Douglas R (2005) Control and learning of ambience by an intelligent building. IEEE Trans Syst Man Cybern A Syst Hum 35(1):121–132. https://doi.org/10.1109/tsmca.2004.838459. ISSN 1083-4427
Rupp RF, Vásquez NG, Lamberts R (2015) A review of human thermal comfort in the built environment. Energy Build 105:178–205
Schellen L, Loomans M, de Wit M, Van Marken Lichtenbelt W (2013) The influence of different cooling techniques and gender on thermal perception. Build Res Inform 41(3):330–341
Schweiker M, Brasche S, Bischof W, Hawighorst M, Voss K, Wagner A (2012) Development and validation of a methodology to challenge the adaptive comfort model. Build Environ 49:336–347
Soyguder S, Alli H (2010) Fuzzy adaptive control for the actuators position control and modeling of an expert system. Expert Syst Appl 37(3):2072–2080
Acknowledgements
The authors wish to acknowledge the financial support of project DACAR (Ref. BIA2016-77431-C2-1-R) funded by the Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad (MINECO) and the project ME4CA (Ref. P11-TEP-7247) funded by the Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Barbadilla-Martín, E., Guadix, J., Cortés, P., Rodríguez-Palero, M. (2020). Fuzzy Logic for the Improvement of Thermal Comfort and Energy Efficiency in Non-residential Buildings. In: de Castro, R., Giménez, G. (eds) Advances in Engineering Networks. ICIEOM 2018. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-44530-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-44530-0_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44529-4
Online ISBN: 978-3-030-44530-0
eBook Packages: EngineeringEngineering (R0)