Coupling a neural network temperature predictor and a fuzzy logic controller to perform thermal comfort regulation in an office building

Abstract The paper describes the application of a combined neuro-fuzzy model for indoor temperature dynamic and automatic regulation. The neural module of the model, an auto-regressive neural network with external inputs (NNARX), produces indoor temperature forecasts that are used to feed a fuzzy logic control unit that simulates switching the heating, ventilation and air conditioning (HVAC) system on and off and regulating the inlet air speed. To generate an indoor temperature forecast, the NNARX module uses weather parameters (e.g., outdoor temperature, air relative humidity and wind speed) and the indoor temperature recorded in previous time steps as regressors. In its current state, the fuzzy controller is only driven by the indoor temperature forecasted by the NNARX module; no variations in indoor heat gains or occupants' clothing and behavior were considered for driving the controller. The main goal of this paper is to demonstrate the effectiveness of the hybrid neuro-fuzzy approach and the importance of efficiently designing the temperature forecast model, especially with respect to the selection of the order of the regressor for each of the external and internal parameters used. Therefore, a differential entropy-based method was applied in this study, which provided good forecasting performances for the NNARX model.

[1]  Masaharu Mizumoto,et al.  Some Properties of Fuzzy Sets of Type 2 , 1976, Inf. Control..

[2]  V. Lo Brano,et al.  Forecasting daily urban electric load profiles using artificial neural networks , 2004 .

[3]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[4]  Anastasios I. Dounis,et al.  Design of a fuzzy system for living space thermal-comfort regulation , 2001 .

[5]  Abdullatif Ben-Nakhi,et al.  Energy conservation in buildings through efficient A/C control using neural networks , 2002 .

[6]  M. Cellura,et al.  Nonlinear Black-Box Models for Short-Term Forecasting of Air Temperature in the Town of Palermo , 2011 .

[7]  J. Stoops,et al.  Indoor Thermal Comfort, an Evolutionary Biology Perspective , 2006 .

[8]  V. Lo Brano,et al.  Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area , 2008 .

[9]  Andrew Kusiak,et al.  Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm , 2011 .

[10]  M. Santamouris,et al.  On the impact of urban climate on the energy consumption of buildings , 2001 .

[11]  D. Kolokotsa,et al.  Advanced fuzzy logic controllers design and evaluation for buildings’ occupants thermal–visual comfort and indoor air quality satisfaction , 2001 .

[12]  Christopher Chang,et al.  Indoor air quality and human health , 2004, Clinical reviews in allergy & immunology.

[13]  Bertil Thomas,et al.  Neural Network Models for Predictive Climate Control in Intelligent Buildings , 2004 .

[14]  Agnieszka Zalejska-Jonsson,et al.  Impact of perceived indoor environment quality on overall satisfaction in Swedish dwellings , 2013 .

[15]  A Thörn,et al.  The sick building syndrome: a diagnostic dilemma. , 1998, Social science & medicine.

[16]  Haralambos Sarimveis,et al.  Optimization of window-openings design for thermal comfort in naturally ventilated buildings , 2012 .

[17]  B W Olesen,et al.  International standards for the indoor environment. , 2004, Indoor air.

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

[19]  Mohcine Zouak,et al.  A comparison of linear and neural network ARX models applied to a prediction of the indoor temperature of a building , 2004, Neural Computing & Applications.

[20]  Gianfranco Rizzo,et al.  The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller , 2004 .

[21]  Yusuf Yildiz,et al.  Identification of the building parameters that influence heating and cooling energy loads for apartm , 2011 .

[22]  Farrukh Nagi,et al.  RLF and TS fuzzy model identification of indoor thermal comfort based on PMV/PPD , 2012 .

[23]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[24]  Per Fahlén,et al.  Estimation of operative temperature in buildings using artificial neural networks , 2006 .

[25]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[26]  P. Fanger,et al.  Extension of the PMV model to non-air-conditioned buildings in warm climates , 2002 .

[27]  N. Klitsikas,et al.  The effect of the Athens heat island on air conditioning load , 2000 .

[28]  Jie Chen,et al.  Prediction of room temperature and relative humidity by autoregressive linear and nonlinear neural n , 2011 .

[29]  Andrew Kusiak,et al.  Modeling and optimization of HVAC systems using a dynamic neural network , 2012 .

[30]  Valerio Lo Brano,et al.  Set up of a monitoring system for a preliminary evaluation of the Urban Heat Island in the town of Palermo , 2008 .

[31]  Antonio Messineo,et al.  Using Recurrent Artificial Neural Networks to Forecast Household Electricity Consumption , 2012 .

[32]  Standard Ashrae.  Thermal Environmental Conditions for Human Occupancy , 1992 .

[33]  Haralambos Sarimveis,et al.  Selection of window sizes for optimizing occupational comfort and hygiene based on computational fluid dynamics and neural networks , 2011 .

[34]  Anastasios I. Dounis,et al.  Intelligent control system for reconciliation of the energy savings with comfort in buildings using soft computing techniques , 2011 .

[35]  Chin-Hsing Cheng,et al.  Variant-frequency fuzzy controller for air conditioning driver by programmable logic controller , 2011, 2011 8th Asian Control Conference (ASCC).

[36]  Hao Huang,et al.  A new zone temperature predictive modeling for energy saving in buildings , 2012 .

[37]  Sean Danaher,et al.  Application of an Artificial Neural Network for Modelling the Thermal Dynamics of a Building’s Space and its Heating System , 2002 .

[38]  Antonio Messineo,et al.  Performance evaluation of hybrid RO/MEE systems powered by a WTE plant , 2008 .

[39]  Domenico Panno,et al.  Potential applications using LNG cold energy in Sicily , 2008 .

[40]  T. Schreiber,et al.  Surrogate time series , 1999, chao-dyn/9909037.

[41]  Isaac Turiel,et al.  Indoor air quality and human health , 1985 .

[42]  Richard de Dear,et al.  Thermal comfort in outdoor and semi-outdoor environments , 2005 .

[43]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[44]  M. M. Gouda,et al.  Quasi-adaptive fuzzy heating control of solar buildings , 2006 .

[45]  Weiwei Liu,et al.  A neural network evaluation model for individual thermal comfort , 2007 .

[46]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[47]  Nathan Mendes,et al.  Predictive controllers for thermal comfort optimization and energy savings , 2008 .

[48]  L. Györfi,et al.  Nonparametric entropy estimation. An overview , 1997 .

[49]  P. Fanger.  Moderate Thermal Environments Determination of the PMV and PPD Indices and Specification of the Conditions for Thermal Comfort , 1984 .

[50]  Niels Kjølstad Poulsen,et al.  Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook , 2000 .

[51]  Danilo P. Mandic,et al.  A differential entropy based method for determining the optimal embedding parameters of a signal , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[52]  P. Wargocki,et al.  Literature survey on how different factors influence human comfort in indoor environments , 2011 .