Quasi-adaptive fuzzy heating control of solar buildings

Gouda, M. M.; Danaher, S.; Underwood, Chris

doi:10.1016/j.buildenv.2005.07.008

Cited by 48 publications

(21 citation statements)

References 12 publications

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“…The results obtained from the proposed model have been compared with two models based on higher order statistics; the fuzzy model provides better results in the prediction of the daily solar radiation in terms of statistical indicators. Gouda et al (2006) investigated the development of a quasi-adaptive fuzzy logic controller for space heating control in solar buildings. The main aim of the controller is to reduce the lagging overheating effect caused by passive solar heat gain to a room space.…”

Section: Applications Of Fuzzy Logicmentioning

confidence: 99%

Artificial Intelligence Techniques in Solar Energy Applications

Kalogirou¹,

Şencan²

2010

Solar Collectors and Panels, Theory and Applications

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Section: Applications Of Fuzzy Logicmentioning

confidence: 99%

Artificial Intelligence Techniques in Solar Energy Applications

Kalogirou¹,

Şencan²

2010

Solar Collectors and Panels, Theory and Applications

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“…In various studies, the predicted indoor air temperature from an ANN-based controller and its difference from the set-point temperature were used as inputs for the fuzzy controller. This ANN-based quasi-adaptive fuzzy control method reduced overshoots of the air temperature and energy consumption [10]. Another study proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS) for conditioning the indoor air temperature and humidity by adjusting the damper angles in the heating ventilation and air-conditioning system [11].…”

Section: Introductionmentioning

confidence: 99%

Determining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings

2013

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This study examines the performance and adaptability of Artificial Neural Network (ANN)-based thermal control strategies for diverse thermal properties of building envelope conditions applied to residential buildings. The thermal performance using two non-ANN-based control logics and two predictive ANN-based control logics was numerically tested using simulation software after validation. The performance tests were conducted for a two-story single-family house for various envelope insulation levels and window-to-wall ratios on the envelopes. The percentages of the period within the targeted ranges for air temperature, humidity and PMV, and the magnitudes of the overshoots and undershoots outside of the targeted comfort range were analyzed for each control logic scheme. The results revealed that the two predictive control logics that employed thermal predictions of the ANN models achieved longer periods of thermal comfort than the non-ANN-based models in terms of the comfort periods and the reductions of the magnitudes of the overshoots and undershoots. The ANN-based models proved their adaptability through accurate control of the thermal conditions in buildings with various architectural variables. The ANN-based predictive control methods demonstrated their potential to create more comfortable thermal conditions in single-family homes compared to non-ANN based control logics.

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“…Artificial neural networks (ANNs) have been widely used to forecast indoor and outdoor air temperature in building applications, sometimes coupled with fuzzy logic systems [27]. However, an adequate literature on the coupling of fuzzy logic and a neural network in order to guarantee comfort evaluation is missing.…”

Section: State-of-the-artmentioning

confidence: 99%

A Dynamic Fuzzy Controller to Meet Thermal Comfort by Using Neural Network Forecasted Parameters as the Input

et al. 2014

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Heating, ventilating and air-conditioning (HVAC) systems are typical non-linear time-variable multivariate systems with disturbances and uncertainties. In this paper, an approach based on a combined neuro-fuzzy model for dynamic and automatic regulation of indoor temperature is proposed. The proposed artificial neural network performs indoor temperatures forecasts that are used to feed a fuzzy logic control unit in order to manage the on/off switching of the HVAC system and the regulation of the inlet air speed. Moreover, the used neural network is optimized by the analytical calculation of the embedding parameters, and the goodness of this approach is tested through MATLAB. The fuzzy controller is driven by the indoor temperature forecasted by the neural network module and is able to adjust the membership functions dynamically, since thermal comfort is a very subjective factor and may vary even in the same subject. The paper shows some experimental results, through a real implementation in an embedded prototyping board, of the proposed approach in terms of the evolution of the inlet air speed injected by the fan coils, the indoor air temperature forecasted by the neural network model and the adjusting of the membership functions after receiving user feedback.

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Quasi-adaptive fuzzy heating control of solar buildings

Cited by 48 publications

References 12 publications

Artificial Intelligence Techniques in Solar Energy Applications

Artificial Intelligence Techniques in Solar Energy Applications

Determining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings

A Dynamic Fuzzy Controller to Meet Thermal Comfort by Using Neural Network Forecasted Parameters as the Input

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