HVAC Control System Using Predicted Mean Vote Index for Energy Savings in Buildings

Abstract

Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of these two factors, energy efficiency and comfort, is a considerable challenge for building operations. This paper introduces a design approach to control an HVAC, focused on an energy consumption reduction in the operation of the HVAC system of a building. The architecture was developed using a Raspberry Pi as a coordinator node and wireless connection with sensor nodes for environmental variables and electrical measurement nodes. The data received by the coordinator node is sent to the cloud for storage and further processing. The control system manages the setpoint of the HVAC equipment, as well as the turning on and off the HVAC compressor using an XBee-based solid state relay. The HVAC temperature control system is based on the Predicted Mean Vote (PMV) index calculation, which is used by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) to find the appropriate setpoint to meet the thermal comfort of 80% of users. This method combines the values of humidity and temperature to define comfort zones. The coordinator node makes the compressor control decisions depending on the value obtained in the PMV index. The proposed PMV-based temperature control system for the HVAC equipment achieves energy savings ranging from 33% to 44% against the built-in control of the HVAC equipment, when operating with the same setpoint of 26.5 grades centigrade.

IoT-Based Air Conditioning Control System for Energy Saving

“…While the automatic-to-manual operation saved 1.68 kWh (20.44%), 1.78 kWh (22.14%), and 1.66 kWh (20.24%), respectively, for the ordinary, Simpson's composite rule, and trapezoidal composite rule methods. These values of the energy-saving were lower than that of the dynamic setting temperature, 59%.1% [4], ventilation and variation law, 30% [5], M-cycle evaporative cooling, 38% [8], exhausted air recovering energy, 50% [10], preference map, 57.38% [25], 33%-44% energy saving of the PMV-based temperature control system [27], and remote computer room, 40% [28]. However, these values of percentage energy saving are more significant than that of the adaptive model, 8.6% [6], with stratum ventilation, 6.4% [12], and 4%-12.4% power saving using the condenser evaporative cooling of split-type AC [14].…”

“…Using an IoT basis, a preference map reduces 57.38% energy consumption [25], A PMV-based temperature control system saved 33%-44% of consumed energy [27], and an IoT-based computer room achieved 16.64% energy-saving [28]. The IoT prototype saved up to 20% of energy in HVAC systems [29].…”

An Experimental Study of Thermal Comfort and Indoor Air Quality—A Case Study of a Hotel Building

“…The air velocity was assumed to be 0.1 m/s. The values of these parameters were selected based on the ASHRAE standard 55 [9] and other papers [54][55][56]. In several papers [57][58][59], the mean radiant temperature was assumed to be equal to the air temperature.…”

Algorithmic Generation of Building Typology for Office Building Design

“…In addition, cooling and heating energy optimization is the most popular research area [32]. There is also a lot of research on the optimal operation of HVAC systems to save energy [33]. In some cases, CFD optimization is coupled to achieve the appropriate occupant comfort [34].…”