The rapid improvement in the standard of living requires more detailed and sophisticated methods of evaluating comfort conditions. But, maintaining thermal comfort conditions in confined environments may require complex regulation procedures and the proper management of heating, ventilating and air conditioning (HVAC) systems. In turn, the requirements for indoor thermal comfort do not necessarily coincide with those of energy saving purposes, which in the last years are becoming a crucial issue owing to the enactment of the European Energy Performance of Buildings Directive (EPBD).
The aim of this work is to compare different indoor control thermal comfort strategies in view of the evaluation of the energy cost of quality in buildings.
In particular, a new PID-fuzzy controller is presented and compared with a classic ON–OFF controller. The performances of the two controllers are quantified and compared by means of two cost functions that are based on the quadratic forms of the overall energy required by the thermal fluid and of the deviation from the preferred set point of the predicted mean vote (PMV). It is found that the application of the PID-fuzzy controller results in lower costs of energy input and lower deviation from set point of PMV
Although the indoor thermal comfort issues have been dealt with widely in scientific literature [1], [2], [3], [4], [5], [6] and [7], it is now gaining a more and more rising attention among designers of heating, ventilating and air conditioning (HVAC) systems, particularly due to the enactment of the European Energy Performance of Buildings Directive (EPBD) [6]. This directive, in fact, besides promoting directly the energy performances of the buildings, the reduction of the conventional fuels consumption and the decrease in greenhouse gas emissions to the atmosphere, indirectly gives emphasis to measures and actions devoted to the increasing of the indoor performances [8] and [9], even affecting the energy demand of buildings. It is well known that the increasing demand for energy management in buildings prompts to the development of control methodologies that could improve energy efficiency of building-HVAC systems [10], [11], [12] and [13]. Nevertheless, the most of the conventional control strategies for indoor comfort, proposed up to now, are limited to ON–OFF and conventional proportional, integrative and derivate (PID) methods, which actually show some limitations. In the ON–OFF method, for example, the controlled variable swings continuously and thermal comfort is regulated only by the indoor temperature; the classical PID control, on the other hand, is not well suitable in following the disturbances and modifications induced to the indoor climate by the varying thermal requirements of different buildings.
Hence it follows the need for developing a control method which could improve the energy efficiency of building while, simultaneously, indoor comfort is maintained within acceptable limits.
Despite the general thought, the need for a more comfortable indoor environment and the need for energy saving purposes could not be always in conflict although, generally speaking, the improving of the indoor conditions could result in a greater consumption of energy.
At this regard, although some interesting works related to the artificial intelligence topics have shown that fuzzy systems and neural networks can simultaneously control both requirements, in this way contributing to reduction of energy consumption and guarantying acceptable indoor comfort conditions [14], [15], [16], [17] and [18], we need to better matching the purposes of the energy saving and of the indoor thermal conditions for people. In other words, we must dispose of effective and simple tools of analysis that contemporary take into account both requirements in the building and HVAC design process.
In this aim, we will present a comparison between the classic control strategies (ON–OFF controller) and a PID-fuzzy controller: having in mind the energy cost related with the achievement of indoor thermal comfort conditions, we will adopt a couple of cost functions, as suggested by Ardheali et al. [17], that is the penalty associated with the deviation from the optimal comfort set point of the selected comfort index, and the penalty associated with the energy consumption for heating purposes.
Concerning the application here presented, from the comparison of these regulating systems it is possible to draw the following conclusions.
1.
Referring to comfort considerations, the most suitable indicator to be utilized in order of ranking the performances of the heating systems is that one proposed within the standard EN 15251 [8] (Annex F – Method C), I2c. This indicator, as discussed in the previous sections, shows a better prevalence in comparison with the I1c index. Concerning the energy cost considerations, the most suitable indicator is I2e, that is the summation of the thermal power supplied by the heat-distributing devices over the whole heating season. In fact, as indicated by means of the energy consumption analysis previously reported, the “energy cost function” indicator I1e is not well suitable for comparisons among PID-fuzzy and ON–OFF controllers, since it, depending on its analytical definition, emphasizes the peaks of the required power with respect to the running time of the equipment.
2.
The importance of a proper sizing of the equipment is singled out by the use of the indicators here selected for analyzing its energy cost. In fact, for a correct size of the equipment, a PID-fuzzy controller seems to lead to less costs for the management of the equipment; on the contrary, an over sizing of the power of the equipment doesn’t justify the use of a such sophisticated regulator.
Anyway, although the ON–OFF systems seem to induce less energy consumptions in the cases of oversized heating system (that, in any case should be avoided by a correct designing point of view), they lead to poorer comfort conditions to people. This justify the tendency of designers and technicians toward a less utilization of the ON–OFF regulation controllers. Moreover, such approach does meet in a more proper way the requirements of the European directive concerning the energy performance of building [6] where, in the current release, regulation and control issues are not suitably taken into account.
Although the results here discussed seem to be quite interesting, it must be observed that final conclusions concerning the supremacy of a control system should be only assumed after a systematic comparison among different strategies, by utilizing a more accurate modelling of the building-HVAC system and of the climate condition on the site, supported by more extensive sets of experimental data.
Appendix. Simplified test dynamic model
In order to evaluate and to compare the performances of different types of controllers for HVAC systems, in this section a simplified dynamic model of the building-HVAC system has been developed. Anyway this simplified approach doesn’t constitute a prejudice for the generality of the present analysis, as it will be shown in details in the following.
The instantaneous energy balance of a building-HVAC system in the heating season, can be expressed as a function of the thermal losses towards the outside, View the MathML sourceq˙t, of the thermal energy stored up within the indoor environment, View the MathML sourceq˙a, and of the amount of energy supplied by the heat-distributing devices
In the following, each term of Eq. (A.1) will be briefly described.
Let us suppose that the thermal losses towards the outside depend only on the thermal flow crossing the envelope surfaces of the building. Moreover, in the present application we suppose that the thermal losses by ventilation are negligible. Under these conditions, the thermal losses towards the outside can be calculated as follows:
where
•
HT is the global heat transfer coefficient of the building envelope (W/K);
•
θa is the indoor air temperature (°C), function of time;
•
θout is the outdoor air temperature (°C), function of time.
Once more, for the sake of simplifying the analysis, let us suppose that the storage term depends only on the indoor air thermal capacity. It follows that the enthalpy variations due to the phase changes and the wall thermal capacity can be neglected. As that, we can say that heat storage mainly depends on the indoor air thermal capacity, without affecting the generality of the analysis. This simplification is assumed for since we are here particularly interested in the fast variations of air temperature values. In other words, we will investigate the changes of the air temperature value, which occur with a low time constant, compared to that one of the wall. This simplification leads to the following expression:
where
•
ca is the specific heat of the indoor air (J/kg K);
•
M is the air mass in the considered volume (kg);
•
ρa is the air density (kg/m3);
•
V is the indoor building volume (m3).
The heating capacity of a heat-distributing device in Nonstandard Conditions [25] and [26] can be calculated by the following expression:
