Modelling of Heat Flux in Building Using Soft-Computing Techniques

Sedano, Javier; Villar, José R.; Curiel, Leticia; Cal, Enrique de la; Corchado, Emilio

doi:10.1007/978-3-642-13033-5_65

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…The results from subsequent studies [34,37,38] that used a procedure for detecting thermal insulation failures in buildings in operation provide a clue for solving the two above-mentioned drawbacks: the use of CMLHL to find the most relevant variables for estimating in-room temperature evolution. Using these ideas, the four-step procedure detailed in previous section has been improved.…”

Section: A Multi-step Procedures For Detecting Thermal Dynamics In Buimentioning

confidence: 99%

“…Nevertheless, a new post-processing step is introduced using CMLHL as a dimensionality reduction technique to choose the most relevant features in order to determine the indoor temperature. Finally, the Simulated Annealing step for training a polynomial model was replaced with an ANN model, as proposed in [34,37,38]. The differences between the two methods are depicted in Figure 1.…”

Section: A Multi-step Procedures For Detecting Thermal Dynamics In Buimentioning

confidence: 99%

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Learning and training techniques in fuzzy control for energy efficiency in buildings

Sedano¹,

Villar²,

Curiel³

et al. 2011

Logic Journal of IGPL

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A novel procedure for learning Fuzzy Controllers (FC) is proposed that processes energy efficiency issues and uses them to distribute electrical energy to heaters in an electrical energy heating system. Energy rationalisation 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 minimises 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.

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Section: A Multi-step Procedures For Detecting Thermal Dynamics In Buimentioning

confidence: 99%

Section: A Multi-step Procedures For Detecting Thermal Dynamics In Buimentioning

confidence: 99%

Learning and training techniques in fuzzy control for energy efficiency in buildings

Sedano¹,

Villar²,

Curiel³

et al. 2011

Logic Journal of IGPL

View full text Add to dashboard Cite

show abstract

Dynamic Fuzzy-Neural based tool formonitoring and predicting patients conditions using selected vital signs

Schatz

Schneider

Abatti

et al. 2015

IFS

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Modelling of Heat Flux in Building Using Soft-Computing Techniques

Cited by 2 publications

References 10 publications

Learning and training techniques in fuzzy control for energy efficiency in buildings

Learning and training techniques in fuzzy control for energy efficiency in buildings

Dynamic Fuzzy-Neural based tool formonitoring and predicting patients conditions using selected vital signs

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