The maintenance of particular thermo-hygrometric comfort levels is linked to energy consumption and consequent energy cost (Corgnati et al, 2006a). This apparently obvious statement is, in fact, quite profound:
it is useless to express energy consumption for microclimate control in a building without relating such consumption to the microclimatic quality assessed for the environment (Corgnati et al, 2008a). The aim of thorough management of the building plant system is to
Table 3.4 Annual use of electricity for office equipment per work place in kWh and per conditioned area in
Per work Per m2 place conditioned area Floor area per person 10m² 15m² 20m² With energy efficient equipment 120 12 8 6 With typical equipment 230 23 15 12
satisfy thermo-hygrometric and air quality requirements with the minimum possible consumption of non-renewable resources, and therefore to minimize the costs.
The first step is to clearly define the thermo-hygrometric quality expected and measured in the environment. In fact, it is not rare that contracts for heating management services set standard values for temperature, relative humidity and air quality with narrow tolerance intervals, often without considering that narrow thermal tolerance intervals lead to unavoidably high energy consumption. Moreover, it is often forgotten that modern theories on adaptive thermal comfort allow wider tolerance intervals, and modifications of the environment parameter values enable one to reduce energy costs.
From a normative viewpoint, the indoor environment classification in terms of microclimatic quality is dealt with in the draft standard EN 15251, which has recently been developed by the European Committee for Standardization. This draft deals in general terms with the theme of indoor environmental quality, including thermo-hygrometric, visual and acoustic comfort, and air quality. With reference to thermo-hygrometric comfort aspects and, in particular, the classification used for the thermo-hygrometric quality assessment, the draft provides for the subdivision of comfort into classes, corresponding to three different levels of environmental quality (class 1 or A is characterized by the narrowest tolerance interval and the highest thermo-hygrometric comfort; the attribution of a number or a letter to the class is being discussed). In particular, the draft standard defines the thermo-hygrometric parameters, divided according to building use, which have to be adopted at the design stage, in order to measure the building plant system during both summer and winter seasons. Figure 3.9
shows the temperature intervals with their respective classes, proposed for air-conditioned office spaces.
Figure 3.9 clearly shows that the acceptable temperature variation interval increases progressively from A to C class: the interval is of the order of 2°C for class A (both in winter and in summer), while for class C it extends to 6°C in winter and to 5°C in summer.
Obviously, the extension of the interval has a direct impact, not particularly on the set-point temperature value, which must be maintained in the environment, but on the acceptable regulation bands.
Therefore, it is clear that the purpose of maintaining a certain temperature level must be supported by equipment able to achieve the objective.
For instance, it is impossible to keep an office space in class A during the whole year without installing an air-conditioning system able to control the temperature within the narrow preset intervals. However, in many cases the requirement of mechanical climate control is limited to only one particular period of the year (i.e.
schools just need a heating plant), or more periods of the year alternated with natural climate control periods (i.e. offices need heating in winter, cooling in summer and natural climate control in mid-season). The intervals proposed in Figure 3.9, as clearly indicated, refer to the design of fully mechanically controlled environments.
What happens when we want to examine the actual microclimatic quality of an indoor-operating environment throughout the whole year?
The in-situ study of microclimatic quality through long-term measurements was carried out according to a research design guide (ASHRAE RP884), focused, in particular, on the theme of thermal quality levels. The obtained results provided further impetus for a number of other in-situ case studies, which led to the so-called
‘adaptive comfort’ theory, particularly suitable for describing the thermal comfort conditions in non-fully mechanically controlled environments (de Dear and Brager, 1998).
This theory comes from the assumption that the comfort sensation can not only be explained by the thermal balance equation between the human body and the surrounding environment (as per Fanger’s 1982 classic comfort model, perfectly suitable for fully mechanically controlled environments); but it must also consider other factors (behavioural, cultural, social and contextual), which may affect the thermal sensation (de Dear and Brager, 2002).
Figure 3.9 Operating temperature intervals recommended for the design of fully mechanically controlled office spaces,
during summer and winter seasons
The impact of such factors increases in a ‘naturally’
controlled environment, where the microclimate is not
‘artificially’ created and controlled by a plant (fully mechanically controlled), but is the result of the user’s direct action (even just partially, if we consider natural ventilation) (Brager and de Dear, 2000). The conducted studies have demonstrated that people have a higher tolerance for ‘less narrow’ microclimatic conditions (in terms of extension of the acceptable temperature intervals) in naturally ventilated environments. In fact, people can activate behavioural, physiological and psychological regulation mechanisms that lead to a wider acceptability of thermo-hygrometric conditions.
One of the main results of the ASHRAE RP884 research project is represented by the diagrams showing how the operating temperature intervals for the thermal quality classes (class A, B and C) vary according to the average monthly outdoor temperature, both in ‘fully mechanically controlled’ and ‘naturally ventilated’ environments (see Figures 3.10 and 3.11).
These figures can be opportunely used as a basis to represent the thermal monitoring results of an indoor environment (i.e. an office or a classroom), and consequently to assess thermal quality during occupancy hours.
The example in Figure 3.12 shows the results of long-term microclimate monitoring of a ‘hybrid’
environment: heating by radiators and natural ventilation by opening windows during the winter season, and cooling by natural ventilation and opening windows in the mid and summer season (Ansaldi et al, 2006). For this type of environment, typical of many Italian buildings, a hypothesis of temperature intervals and microclimatic quality classes was proposed according to the above-mentioned research methods (Corgnati et al, 2008b). In particular, Figure 3.12 shows the results of a monitoring campaign, displaying indoor temperature values measured in relation to the thermal quality classes (A and B). Two parts are clearly distinguished in the figure: the left side of the diagram (mechanical climate control during the heating season) shows that values and microclimate class intervals remain constant despite varying outdoor temperature, while the right side of the diagram (no mechanical climate control) shows that values and microclimate class intervals vary according to outdoor temperature variations.
Therefore, such diagrams can be opportunely used as a basis to represent thermo-hygrometric monitoring results and, consequently, to assess the obtained environmental quality.
The thermal quality assessment of the environment can be expressed by a synthetic index called the performance index (PI) (Corgnati et al, 2006b), which
Figure 3.10 Intervals for thermal quality classes in ‘fully mechanically controlled’ environments (as per ASHRAE RP884)
represents the percentage of measured values falling within the acceptability interval of a given class. Therefore, this parameter indicates how often the examined environment is exposed to acceptable thermal conditions.
With reference to the measurements shown in Figure 3.12, the performance index of class A intervals is 84 per cent during the heating period, and decreases to 69 per cent if we consider the whole analysis period
(from October to July). This methodology of data representation and analysis is very effective as it ensures representation clarity and easy comprehension of the thermal quality index. The microclimatic quality analysis described above can be conducted while monitoring the energy consumption for air conditioning in order to prove their correlation with the obtained thermal quality level.
Figure 3.11 Intervals for thermal quality classes in ‘naturally ventilated’ environments (as per ASHRAE RP884)
Notes: CT = operating comfort temperature; TOMM= average monthly outdoor temperature.
Source: as in Corgnati et al (2008b)
Figure 3.12 Temperature values and thermal quality classes proposed in the hypothesis of a thermal model for hybrid environments
Ansaldi, R., Corgnati, S. P. and Filippi, M. (2006)
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Methodology and application to an office building’, CLIMAMED 2006 International Conference, Lyon, France, October 2006
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Fundamentals, Atlanta, Chapter 32.
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Analytical Determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria, EN ISO 7730 CEN (2007a) Indoor Environmental Parameters for Assessment
of Energy Performance of Buildings, Addressing Indoor Air Quality, Thermal Environment, Lighting and Acoustics, EN 15251
CEN (2007b) Energy Performance of Buildings: Overall Energy Use and Definition of Ratings, EN 15603
CEN (2007c) Energy Performance of Buildings: Methods for Expressing Energy Performance and for Energy Certification of Buildings, EN 15217
CEN (2007d) Thermal Performance of Buildings: Calculation of Energy Use for Space Heating and Cooling, EN ISO 13790
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‘Energy demand for space heating in existing school buildings: Results of a field survey’, in Proceedings of the Indoor Climate of Buildings, ISIAQ, Slovakia
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EN 15193 (2007) BS EN 15193: 2007, ‘Energy performance of building: Energy requirements for lighting’, BSI, London
Energy modelling is the discipline that models the energy flows in buildings and between a building and its (local) environment, with the aim of studying the heat and mass flow within buildings and their (sub)systems under given functional requirements that the building must satisfy. Most of the current models are computational in nature. This means that the models are implemented in the form of a computer simulation that replicates a part of physical reality in the machine. To do this efficiently, energy models idealize, quantify and simplify the behaviour of real-world systems such as buildings by describing them as a set of internal variables, distinct system boundaries and external variables. The application of physical laws leads to a set of relations between the variables of this physical model, which together constitute the mathematical model. This is then coded in some programming language and subsequently run as a computer programme (commonly named tool). In energy modelling, the area of interest is the thermal behaviour of buildings, especially in terms of energy efficiency and thermal comfort.
Energy modelling is a key element of the broader discipline called building simulation, a domain that, apart from thermal aspects, also studies (day)lighting, moisture, acoustics, airflow and indoor air quality. The discipline of building simulation first emerged during the 1960s. During this period, research efforts focused on the study of fundamental theory for building simulation, mostly for energy transfer. During the 1970s, the new field matured and expanded, driven by the energy crisis of those years. Most research was
devoted to the development of algorithms for heating load, cooling load and energy transfer simulation.
During the 1980s, the effects of the energy crisis waned. However, this effect was compensated for by advancements in personal computers, which made building simulation widely accessible. As a result, research efforts now concentrated on programming and testing computational tools. In the same period, natural selection set in: only tools that had active support from their makers (maintenance, updating, addition of desired new features) were able to survive.
Finally, during the late 1980s and the 1990s, the field of building simulation broadened with the development of new simulation programmes that were able to deal with lighting, acoustics and airflow problems. While energy modelling is the most prominent field within building simulation, it is closely related to modelling the aspects that have a direct impact on energy use and thermal comfort. This is particularly so for the study of airflow and lighting since airflow impacts upon ventilation and infiltration losses, daylighting is coupled with solar gain, and artificial lighting contributes to internal gain and, thus, affects heating and cooling loads. Such interaction becomes especially interesting within innovative buildings such as the biomes of the Eden Project (see Figure 4.1), where a large space is subject to a mix of natural ventilation and mechanical ventilation, novel building skin elements are used, and indoor air criteria are different from normal – in this case catering for plant comfort rather than human comfort.
A good in-depth discussion of the basics of energy modelling is provided by Clarke (2001); more advanced topics in simulation are discussed by Malkawi