Experimental testing of a system for the energy-efficient sub-zonal heating management in indoor environments based on PMV

Abstract A fine-grained regulation of the HVAC emitters, capable of providing heat and cool only where effectively needed, can lead to a significant energy saving. This paper presents the results from an experimental test of an energy-efficient sub-zonal heating management system, based on an innovative comfort sensor. The objective is to demonstrate how the real-time PMV (Predicted Mean Vote) measurement of different positions in a room can be used to apply optimal rules for the climate control. The case study is an office, located in Central Italy, equipped with two separately controllable electrical heaters. The heating system has been coupled with a low-cost, IR-based comfort sensor, named Comfort Eye, to regulate the heating output of each heater in function of the local comfort conditions. A PID (Proportional–integral–derivative) controller, tuned by fuzzy logic, uses the PMV measured in the respective sub-zone as controlled variable, regulates the power of each heater. The system ran for one winter day and results have been compared with a reference condition, representative of the typical ON/OFF control of the room. The reference condition has been created with the same heating system, but without the sub-zonal division. The comparison, considering the specific application presented, turned out that the sub-zonal control system could achieve an energy saving up to 17% with respect to the typical ON/OFF control with a slight improvement of thermal comfort, reduced deviation from the neutral condition (PMV = 0). This shows that the possibility of measuring comfort distributions is crucial to achieve optimal environmental control.

[1]  Costanzo Di Perna,et al.  Comparing the performance of on/off, PID and fuzzy controllers applied to the heating system of an energy-efficient building , 2016 .

[2]  Gian Marco Revel,et al.  Evaluation in a controlled environment of a low-cost IR sensor for indoor thermal comfort measurement , 2014 .

[3]  Anand Sivasubramaniam,et al.  Centralized Management of HVAC Energy in Large Multi-AHU Zones , 2015, BuildSys@SenSys.

[4]  Kamel Ghali,et al.  Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm , 2009 .

[5]  António E. Ruano,et al.  Neural networks based predictive control for thermal comfort and energy savings in public buildings , 2012 .

[6]  Zeljko Hocenski,et al.  HVAC control methods - a review , 2015, 2015 19th International Conference on System Theory, Control and Computing (ICSTCC).

[7]  Thananchai Leephakpreeda,et al.  Neural computing thermal comfort index for HVAC systems , 2005 .

[8]  Refrigerating ASHRAE handbook of fundamentals , 1967 .

[9]  Gian Marco Revel,et al.  A tool for the optimal sensor placement to optimize temperature monitoring in large sports spaces , 2016 .

[10]  G. M. Revel,et al.  Perception of the thermal environment in sports facilities through subjective approach , 2014 .

[11]  M. Pietrafesa,et al.  A model for managing and evaluating solar radiation for indoor thermal comfort , 2007 .

[12]  Gian Marco Revel,et al.  Measuring overall thermal comfort to balance energy use in sports facilities , 2014 .

[13]  Chunfeng Yang,et al.  Utilizing artificial neural network to predict energy consumption and thermal comfort level: an indoor swimming pool case study , 2014 .

[14]  Hsu-Cheng Chiang,et al.  Thermal comfort and energy saving of a personalized PFCU air-conditioning system , 2005 .

[15]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[16]  Gionata Cimini,et al.  Indoor thermal comfort control through fuzzy logic PMV optimization , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[17]  Stefano Schiavon,et al.  Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures , 2013 .

[18]  Boris Igor Palella,et al.  On the measurement of the mean radiant temperature and its influence on the indoor thermal environment assessment , 2013 .

[19]  Myoung Souk Yeo,et al.  Effect of MRT variation on the energy consumption in a PMV-controlled office , 2010 .

[20]  J. F. Nicol,et al.  The validity of ISO-PMV for predicting comfort votes in every-day thermal environments , 2002 .

[21]  Ferenc Kalmár,et al.  Interrelation between mean radiant temperature and room geometry , 2012 .

[22]  Athanasios Tzempelikos,et al.  Indoor thermal environmental conditions near glazed facades with shading devices - Part II: Thermal comfort simulation and impact of glazing and shading properties , 2010 .

[23]  Stephanie Gauthier,et al.  Predictive thermal comfort model: Are current field studies measuring the most influential variables? , 2012 .

[24]  Gian Marco Revel,et al.  Development and experimental evaluation of a thermography measurement system for real-time monitoring of comfort and heat rate exchange in the built environment , 2012 .

[25]  Truong Nghiem,et al.  Model-IQ: Uncertainty propagation from sensing to modeling and control in buildings , 2014, 2014 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS).

[26]  Zhimin Du,et al.  Temperature sensor placement optimization for VAV control using CFD–BES co-simulation strategy , 2015 .

[27]  Gian Marco Revel,et al.  Cost-effective technologies to control indoor air quality and comfort in energy efficient building retrofitting , 2015 .

[28]  Ruey Lung Hwang,et al.  Building envelope regulations on thermal comfort in glass facade buildings and energy-saving potenti , 2011 .

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

[30]  Gian Marco Revel,et al.  Development and validation of a low-cost infrared measurement system for real-time monitoring of indoor thermal comfort , 2014 .

[31]  Gian Marco Revel,et al.  A wireless sensor network for intelligent building energy management based on multi communication standards - a case study , 2012, J. Inf. Technol. Constr..