Inteligencia Computacional Aplicada (GICAP)http://hdl.handle.net/10259/38472024-03-28T17:10:51Z2024-03-28T17:10:51ZA soft computing method for detecting lifetime building thermal insulation failuresSedano, JavierCuriel Herrera, Leticia ElenaCorchado, EmilioCal, Enrique de laVillar, José Ramónhttp://hdl.handle.net/10259/85832024-02-06T01:05:26Z2010-04-01T00:00:00ZA soft computing method for detecting lifetime building thermal insulation failures
Sedano, Javier; Curiel Herrera, Leticia Elena; Corchado, Emilio; Cal, Enrique de la; Villar, José Ramón
The detection of thermal insulation failures in buildings in operation responds to the challenge of improving building energy efficiency. This multidisciplinary study presents a novel four-step soft computing knowledge identification model called IKBIS to perform thermal insulation failure detection. It proposes the use of Exploratory Projection Pursuit methods to study the relation between input and output variables and data dimensionality reduction. It also applies system identification theory and neural networks for modeling the thermal dynamics of the building. Finally, the novel model is used to predict dynamic thermal biases, and two real cases of study as part of its empirical validation.
2010-04-01T00:00:00ZLearning and training techniques in fuzzy control for energy efficiency in buildingsSedano, JavierVillar, José RamónCuriel Herrera, Leticia ElenaCorchado, EmilioCal, Enrique de lahttp://hdl.handle.net/10259/85822024-02-06T01:05:28Z2011-02-01T00:00:00ZLearning and training techniques in fuzzy control for energy efficiency in buildings
Sedano, Javier; Villar, José Ramón; Curiel Herrera, Leticia Elena; Corchado, Emilio; Cal, Enrique de la
A novel procedure for learning Fuzzy Controllers (FC) is proposed that concerns with energy efficiency issues in distributing electrical energy to heaters in an electrical energy heating system. Energy rationalization together with temperature control can significantly improve energy efficiency, by efficiently controlling electrical heating systems and electrical energy consumption. The novel procedure, which improves the training process, is designed to train the FC, as well as to run the control algorithm and to carry out energy distribution. Firstly, the dynamic thermal performance of different variables is mathematically modelled for each specific building type and climate zone. Secondly, an exploratory projection pursuit method is used to extract the relevant features. Finally, a supervised dynamic neural network model and identification techniques are applied to FC learning and training. The FC rule-set and parameter-set learning process is a multi-objective problem that minimizes both the indoor temperature error and the energy deficit in the house. The reliability of the proposed procedure is validated for a city in a winter zone in Spain.
2011-02-01T00:00:00ZImproving Energy Efficiency in Buildings Using Machine IntelligenceSedano, JavierVillar, José RamónCuriel Herrera, Leticia ElenaCal, Enrique de laCorchado, Emiliohttp://hdl.handle.net/10259/85802024-02-06T01:05:22Z2009-01-01T00:00:00ZImproving Energy Efficiency in Buildings Using Machine Intelligence
Sedano, Javier; Villar, José Ramón; Curiel Herrera, Leticia Elena; Cal, Enrique de la; Corchado, Emilio
Improving the detection of thermal insulation in buildings –which
includes the development of models for heating and ventilation processes and
fabric gain - could significantly increase building energy efficiency and substantially contribute to reductions in energy consumption and in the carbon
footprints of domestic heating systems. Thermal insulation standards are now
contractual obligations in new buildings, although poor energy efficiency is often a defining characteristic of buildings built before the introduction of those
standards. Lighting, occupancy, set point temperature profiles, air conditioning
and ventilation services all increase the complexity of measuring insulation
efficiency. The identification of thermal insulation failure can help to reduce
energy consumption in heating systems. Conventional methods can be greatly
improved through the application of hybridized machine learning techniques to
detect thermal insulation failures when a building is in operation. A three-step
procedure is proposed in this paper that begins by considering the local building
and heating system regulations as well as the specific features of the climate
zone. Firstly, the dynamic thermal performance of different variables is specifically modelled, for each building type and climate zone. Secondly, Cooperative
Maximum-Likelihood Hebbian Learning is used to extract the relevant features.
Finally, neural projections and identification techniques are applied, in order to
detect fluctuations in room temperatures and, in consequence, thermal insulation failures. The reliability of the proposed method is validated in three winter
zone C cities in Spain. Although a great deal of further research remains to be
done in this field, the proposed system is expected to outperform conventional
methods described in Spanish building codes that are used to calculate energetic
profiles in domestic and residential buildings.
Trabajo presentado en: International Conference on Intelligent Data Engineering and Automated Learning, 2009
2009-01-01T00:00:00ZModelling of Heat Flux in Building Using Soft-Computing TechniquesSedano, JavierVillar, José RamónCuriel Herrera, Leticia ElenaCal, Enrique de laCorchado, Emiliohttp://hdl.handle.net/10259/85782024-02-06T01:05:23Z2010-01-01T00:00:00ZModelling of Heat Flux in Building Using Soft-Computing Techniques
Sedano, Javier; Villar, José Ramón; Curiel Herrera, Leticia Elena; Cal, Enrique de la; Corchado, Emilio
Improving the detection of thermal insulation failures in buildings includes the development of models for heating process and fabric gain -heat flux
through exterior walls in the building-. Thermal insulation standards are now
contractual obligations in new buildings, the energy efficiency in the case of
buildings constructed before the regulations adopted is still an open issue, and
the assumption is that it will be based on heat flux and conductivity measurement. A three-step procedure is proposed in this study that begins by considering the local building and heating system regulations as well as the specific features of the climate zone. Firstly, the dynamic thermal performance of different
variables is specifically modeled. Secondly, an exploratory projection pursuit
method called Cooperative Maximum-Likelihood Hebbian Learning is used to
extract the relevant features. Finally, a supervised neural model and identification techniques are applied, in order to detect the heat flux through exterior
walls in the building. The reliability of the proposed method is validated for a
winter zone, associated to several cities in Spain.
Trabajo presentado en: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2010
2010-01-01T00:00:00Z