Fuzzy logic-based advanced on–off control for thermal comfort in residential buildings

Abstract In this paper, an advanced on–off control method based on fuzzy logic is proposed for maintaining thermal comfort in residential buildings. Due to the time-lag of the control systems and the late building thermal response, an anticipative control mechanism is required to reduce energy loss and thermal discomfort. The proposed controller is implemented based on an on–off controller combined with a fuzzy algorithm. On–off control was chosen over other conventional control methods because of its structural simplicity. However, because conventional on–off control has a fixed operating range and a limited ability for improvements in control performance, fuzzy theory can be applied to overcome these limitations. Furthermore, a fuzzy-based solar energy compensation algorithm can be applied to the proposed controller to compensate for the energy gained from solar radiation according to the time of day. Simulations were conducted to compare the proposed controller with a conventional on–off controller under identical external conditions such as outdoor temperature and solar energy; these simulations were carried out by using a previously reported thermal analysis program that was modified to consider such external conditions. In addition, experiments were conducted in a residential building called Green Home Plus, in which hydronic radiant floor heating is used; in these experiments, the proposed system performed better than a system employing conventional on–off control methods.

[1]  Sašo Medved,et al.  Weather-predicted control of building free cooling system , 2011 .

[2]  Yong Zhang,et al.  Advanced controller auto-tuning and its application in HVAC systems , 2000 .

[3]  Mignon Park,et al.  Simulational Analysis of Adaptive Outdoor Reset Control based on a Fuzzy Target Temperature Gap for a Hydronic Radiant Floor Heating System , 2010 .

[4]  Lin-Shu Wang,et al.  Modeling of hydronic radiant cooling of a thermally homeostatic building using a parametric cooling tower , 2014 .

[5]  Xiaoshu Lü,et al.  A novel dynamic modeling approach for predicting building energy performance , 2014 .

[6]  C. E. Dorgan,et al.  Hourly thermal load prediction for the next 24 hours by ARIMA, EWMA, LR and an artificial neural network , 1995 .

[7]  Michaël Kummert,et al.  A neural network controller for hydronic heating systems of solar buildings , 2004, Neural Networks.

[8]  Guang Geng,et al.  On performance and tuning of PID controllers in HVAC systems , 1993, Proceedings of IEEE International Conference on Control and Applications.

[9]  Constantinos A. Balaras,et al.  Development of a neural network heating controller for solar buildings , 2000, Neural Networks.

[10]  Bjarne W. Olesen,et al.  Radiant Floor Heating In Theory and Practice , 2002 .

[11]  Byung-Cheon Ahn,et al.  Control characteristics and heating performance analysis of automatic thermostatic valves for radiant slab heating system in residential apartments , 2010 .

[12]  R. Garcia-Martinez,et al.  Fuzzy Control For Improving Energy Management Within Indoor Building Environments , 2007, Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007).

[13]  M. Pinar Mengüç,et al.  Thermal Radiation Heat Transfer , 2020 .

[14]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[15]  M. Iqbal.  An introduction to solar radiation , 1983 .

[16]  Xiaoqiang Zhai,et al.  Design and performance of the solar-powered floor heating system in a green building , 2009 .

[17]  Moncef Krarti,et al.  Building Energy Use Prediction and System Identification Using Recurrent Neural Networks , 1995 .

[18]  Myoung-Souk Yeo,et al.  Predictive Control of the Radiant Floor Heating System in Apartment Buildings , 2002 .