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Monitoring heating energy consumption

In document A Handbook of Sustainable Building (Page 75-80)

EN 15603 underlines that it is impossible to compare directly the energy performance indexes obtained from an asset rating and an operational rating.

However, the differences between these two ratings can be useful to evaluate the cumulative effects of the actual conditions of the building in comparison with the standard conditions. The following methods should be considered:

• Assess the compliance with technical and operational rules representative of energy targets.

• Compare the energy performances of various design alternatives for a new building.

• Set an energy performance benchmark for existing buildings.

• Evaluate the effect of possible energy-saving actions on an existing building through the analysis of the pre-intervention consumption and the estimation of the possible post-intervention savings.

• Predict the future energy requirements of a building, or a building stock, on the basis of the actual trend of energy consumptions of different buildings representative of the building stock in question.

Monitoring heating energy consumption

This section focuses on the monitoring, standardization and analysis of primary energy consumption for heating in existing buildings. In this case, the monitoring is

defined as continuous measurements during the whole heating season of those parameters that are significant in describing energy consumption for heating in a building (e.g. the amount of fuel consumed) and essential for standardizing energy consumption (e.g. total heated volume of the building).

In order to attribute a specific consumption index to the building that is able to define its energy performance, an operational rating procedure is applied for the following purposes of:

• continuously monitoring and controlling building energy consumption;

• setting reference consumption values for a building;

• predicting energy consumption for future heating seasons.

The attribution process of reference energy consumptions to each building is particularly important. This data can be effectively used to define costs in energy service contracts. Moreover, the attribution of such an energy index to the building is in compliance with European Directive 2002/91/EC on the energy performance of buildings. As already mentioned, it highlights the need to attribute energy performance indicators to existing buildings, even through the analysis of actual consumption.

According to each specific purpose, it may be more significant either to use energy performance indexes obtained from theoretical consumptions on the basis of the known characteristics of the building plant system (calculated rating), or to refer to the actual metered energy consumptions (operational rating). The indicators obtained from this second approach are particularly suitable to represent the consumptions of an operating building as a result of the ‘building–plant–user’ system dynamics.

In particular, the operational rating procedure is based on the estimation of an index of ‘conventional specific energy consumption for heating purposes’

(Corgnati et al, 2004). Essentially, this estimation procedure provides for the development of the following two phases:

1 data collection;

2 definition of the consumption index.

The first phase consists of collecting data concerning building characteristics (both typological and

geometrical), local climate conditions, heating use conditions, energy consumption and, in particular:

• location of the building;

• shape and type of building;

• geometrical characteristics (gross heated floor area, useful heated surface, etc.);

actual degree days (DD), on a yearly and, if possible, monthly basis;

• fuel type;

• actual primary energy consumption for heating (CE) on a yearly and, if possible, monthly basis;

• actual consumed energy delivered by the heat generator (QP) on a yearly and, if possible, monthly basis;

• duration of the heating period, expressed in hours, on a monthly and yearly basis;

• indoor thermo-hygrometric conditions (T and RH).

In order to analyse a sufficiently significant data sample, it is necessary to collect monitoring data representative of at least three heating seasons. The duration of the heating period is represented by the hours during which the heat generator supplies the consumed energy QP. However, other data can, in some cases, be more appropriate to evaluate the d parameter. For instance, in the case of a heating management service contract, the purchaser can consider it more significant to identify with d the number of hours during which the appropriate minimum temperature conditions must be maintained in order to ensure the occupants’ comfort (i.e. 20°C during occupancy hours). In this case, the aim of the heating management company is to define a heating strategy that enables consumption to be minimized and satisfies the minimum required level of environmental quality (i.e. the above-mentioned 20°C).

The second phase consists of defining the consumption index (Corgnati et al, 2007). For example, in the case of a heating management service based on the purchase/supply of thermal energy delivered by the heat generator, the consumption index referring to the actual energy delivered by the heat generator (QP) is obtained from the following expression:

(1)

The QPs,c index represents the ‘conventional specific energy consumption for heating purposes’, given by the ratio of the delivered thermal energy (measured by heat meters) to the gross heated volume, with reference to the conventional degree days in the examined area (DDc), and the conventional duration of the heating period (dc). The fuel consumption, or in this case the useful delivered energy, is first of all divided by the volume in order to obtain a specific value. The mutual relation between consumption and volume is well known. The example in Figure 3.1 shows a sample of buildings heated with natural gas for the same use (therefore with approximately equal heating period duration) located in the same area (therefore with equal heating seasons and equal effective degree days).

The proposed model for the QPs,cindex assessment provides, in accordance with the European Standard EN 15203 and former authors’ works (Corrado et al, 2004), a linear dependence between the energy consumption for heating and:

• the local degree days;

• the duration of the heating period of the building.

Figure 3.2 shows an example of the monthly metered fuel consumptions in a given building (expressed in cubic metres of natural gas per cubic metre of gross heated volume) as a function of the corresponding actual monthly degree days. The regression line shows a good correlation between the data, as indicated by the R2 value of 0.88. Moreover, the scatter of the data around the regression curve shows how consumptions are influenced by stochastic factors and deviate from perfect linearity. In particular, users’ behaviour may significantly influence the size of endogenous heat supply, the ventilation rate, the incoming solar radiation, etc. Once the monitoring data have been collected, it is always convenient to make an accurate analysis, as shown in Figure 3.2, in order to assess how decisive the influence of the above-mentioned stochastic variables is on the actual consumption of an examined building.

With reference to the other parameter – that is the duration of the heating period (d ) – it is important to note that its actual value is strongly related not only to the occupation time of the building, but also to the thermal dynamics of the ‘building envelope plant’

system (the difference between light and heavy buildings is an example).

QP QP

While assessing the QPs,cindex, it is important to estimate the reliability of the metered energy consumption values. The heat meters, in fact, may often give approximate values due to possible malfunctions and measurement inaccuracies. On the contrary, the metered fuel consumption values are typically more reliable. The reliability of the metered QP values is estimated by calculating, on a monthly and seasonal basis, the efficiency of the heat generator:

(2) The evaluation procedure for generation efficiency has been amply dealt with in a former author’s publication (Corrado et al, 2004). As already mentioned, the index of conventional specific energy consumption for heating purposes QPs,ccan be defined using monitored consumption data representative of at least three

ηp=QP CE

Figure 3.1 Fuel consumption as a function of gross heated volume

Figure 3.2 Specific monthly fuel consumption as a function of actual monthly degree days

Figure 3.3 Consumption data collection chart for a building

heating seasons, and can be useful to define consumptions and costs for a heating management service, or simply to evaluate or predict the energy consumption of future heating seasons. The evaluation procedure presented above has been validated by the authors (see Corgnati and Corrado, 2006).

The procedure is mainly applied to compare the predicted consumption (on the basis of the specific energy consumption QPs,c) with the actual consumption for a given heating season. An example of a data collection chart is shown in Figure 3.3.

The chart in Figure 3.3 is used to collect data concerning both the building and its consumption, as well as climate conditions. It is divided into three main sections:

1 general information;

2 monthly energy and climate data;

3 diagram of comparison between predicted and measured consumptions.

The general information includes data concerning the building (plant code, building name, city and address), climate data (average monthly outdoor temperature, monthly degree days, climatic zone and duration of the heating period), and the main characteristics of the building (category, gross heated volume and fuel type).

The monthly data section includes a table showing conventional and measured quantities. The climatic zone where the building is located is defined on the basis of the number of degree days. The time interval for the conventional duration of the heating period is identified according to the climatic zone. For instance, with reference to buildings in Italian climatic zone E (number of degree days below 3000°Cd), the heating season extends from 15 October to 15 April. The conventional daily number of hours used to calculate the conventional duration of the heating period is set on the basis of the building use (i.e. 6 hours a day for schools and offices, 14 hours a day for residential buildings).

In order to compare the measured and calculated values, it is necessary to correct the ‘conventional data’, according to the climate conditions and actual operating hours. To this end, the ‘corrected conventional specific heat supply’ can be defined as:

(3) The comparison between measured and calculated values is fundamental in order to evaluate the accuracy of the consumption prediction, as shown by the example in Figure 3.4. The diagram represents the corrected conventional heat supply (x axis) and the measured specific heat supply (y axis) for the heating of

QP QP DD

DD d

c c r d

c r c

* =

Figure 3.4 Measured specific heat supply versus corrected conventional specific heat supply (117 school buildings)

117 schools during one entire heating season in climatic zone E. Obviously, in the case where the predicted and measured data are perfectly coincident, the dots representing the examined sample would fall along the bisector of the first quadrant of the Cartesian coordinate system (y = x). This diagram shows that the dots, although scattered around the bisector, tend to concentrate mainly in the lower part of the quadrant, which reveals that the predictive model tends to slightly overestimate actual consumptions.

It is evident that in the case where the above-mentioned predictive model is not applied to the consumption of every single building, but to an entire sample of buildings (i.e. for heating management service in real estate), the comparison between total measured consumptions and total predicted consumptions is more significant.

The histogram in Figure 3.5 shows a comparison between metered and predicted heat supply for a mixed-use group of buildings where a heating management service is carried out. The real estate is basically composed of school buildings, representing over 95 per cent of the real estate consumptions, and some office and residential buildings covering the remaining 5 per cent of consumptions. It is evident that the total estimated consumption predicts with reasonable accuracy the total metered consumption.

The difference of 6 per cent is an acceptable value if we consider the number of stochastic factors that may interfere between predicted and actual consumptions, such as users’ behaviour.

Energy labelling of heating

In document A Handbook of Sustainable Building (Page 75-80)