A virtual thermostat for local temperature control

Abstract

This thesis presents design and operation of a newly developed virtual thermostat to control any desired local temperature in an HVAC controlled space. Conventional HVAC thermostats use a single point temperature sensor at one fixed location in a controlled space to control the temperature of the whole space. The single point temperature sensor does not represent the entire controlled domain. In addition, in most applications only a small zone in the controlled space needs to be controlled (for example where the people are located). Heating and cooling of the unnecessary zones lead to extra energy consumption that can be saved if only the required zones are controlled. Such a control system requires knowledge of the temperature distribution in the whole zone at all times. In this work, we have used computational fluid dynamics (CFD) to determine the distributive temperature inside the controlled space at all times and turn the HVAC system ON and OFF based on the temperature of the desired zone in the space. In this study, we have determined the energy savings by comparing the energy consumption of a conventional thermostat controlled system with a locally controlled one. Several cases with different heat losses, and different inlet air velocities and directions are studied. Energy savings of up to 22% is realized for the cases studied here. In addition, the study showed the effect of person's location on the energy saving using the local control. One of the main problems with such a control system is that the CFD calculations may take a long time (10-24 hours depending on the complexity of the iii problem), which is not practical for real-time temperature control. In order to reduce the computational times and simulate various operating conditions quickly, an artificial neural network (ANN) model is used to train the temperature control system. This resulted in reduction of the computational times to few seconds, making this system practical. This thesis, presents various elements of the newly developed virtual thermostat and its operating principals.iv

A modelling method for large-scale open spaces orientated toward coordinated control of multiple air-terminal units

“…An identification method based on ANN model was proposed to study multi-zone buildings while taking into account thermal interactions effects between neighboring zones (Huang et al 2015). An average temperature around an occupant, numerically obtained by CFD method, was feedback to a local ON/OFF controller to decrease energy consumption (Alhashme and Ashgriz 2016). Zhang et al (2018aZhang et al ( , 2019 proposed a dynamic temperature control based on multi-node model for both stratum ventilation and displacement ventilation.…”

Opportunities for improved space heating energy efficiency from fluid property modifications

“…5 It is not uncommon for heating and cooling flows, in the case of air conditioning systems, to be capable of rates of temperature change 5 to 10 times higher than surroundings heat loss/gains. 6,7 Even with a steady heat demand this creates a dynamic thermal environment where thermal energy is added to a space through the hydronic system until the flow is stopped when the room reaches its setpoint temperature (e.g. through a wall thermostat or thermostatic radiator valve, TRV).…”

“…The importance of what may appear only small changes to such cycling behaviour can have a significant influence on energy consumption (circa 15%). 7 In real-world scenarios where external temperatures, solar irradiance, meteorological conditions and occupant behaviours are fundamentally unsteady, the dynamic behaviour of the heating system is further exacerbated.…”

Differences in temperature measurement by commercial room temperature sensors: Effects of room cooling system, loads, sensor type and position

“…Madsen et al [14] observed a reduced fluctuation of the indoor temperature and a more constant temperature distribution across the room when the thermostat was placed in the centre of the room, compared to when it was placed on the wall. Alhasme and Ashgriz [15] simulated various temperature distributions within a room by using computational fluid dynamics (CFD) and reported that controlling the HVAC system based on the local temperature close to an occupant resulted in a 22% energy saving compared with a thermostat controlled from a single temperature sensor mounted on the wall. Huang et al [16] developed a CFD-aided method to generate a real-time temperature distribution of a room and optimize the location of wireless temperature sensors.…”