Continuous assessment of energy efficiency in commercial buildings using energy rating factors

Energy and Buildings 49 (2012) 78–84

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Continuous assessment of energy efficiency in commercial buildings using energy rating factors Guillermo Escrivá-Escrivá ∗ , Oscar Santamaria-Orts, Fernando Mugarra-Llopis Institute for Energy Engineering, Universitat Politècnica de València, Camino de Vera, s/n, edificio 8E, escalera F, 2a planta, 46022 Valencia, Spain

a r t i c l e

i n f o

Article history: Received 28 October 2011 Received in revised form 12 January 2012 Accepted 15 January 2012 Keywords: Building classification Continuous characterisation Energy efficiency Building in use

a b s t r a c t Engineers often conduct energy audits in buildings in use with the aim of improving the energy consumption of the facilities. The problem with this traditional approach is that the audits take place at a given instant in time. Subsequently, it is necessary to track the amount of energy continuously consumed in buildings in order to deal with an energy efficiency problem. This paper proposes the continuous assessment of energy efficiency in buildings using different energy rating factors. These factors have already been presented in other works by the authors, and the usefulness of these factors for assessing the energy performance of buildings and identifying wasted energy has been proven. This article presents the results of applying the methodology in 55 buildings located on the Universitat Politècnica de València campus during an entire year. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Human energy consumption is currently an important issue to address because of the scarcity of resources, energy price increases, and identified environmental problems [1–5]. Non-industrial buildings are one of the biggest consumers of energy and account for 30–40% of the total primary energy consumed in developed countries, and this consumption is increasing in line with human activity [6]. Country specific regulations developed in Europe suffer several shortcomings [7] that have resulted in problems when evaluating existing buildings, especially at the use stage. These problems have arisen because the use of the facility and the associated maintenance procedures were not properly considered. Analysis of energy consumption at the use stage is very important [8]. It is obvious that the construction characteristics of buildings strongly affect consumption during the life cycle of the building. Furthermore, the way in which facilities are used is also very important when determining the efficiency of a building. An energy audit is a study aimed at improving energy efficiency in buildings in use. These audits consist of an information-gathering stage, analysis, proposal for actions, technical and economic assessment, and a report. Problems arise because these audits are performed at a certain point in time and are not continuous. A building may sharply change its efficiency due to degradation in facilities, actions performed by users, etc.

In a previous work, the authors proposed several indices to perform energy characterisation and make a classification of buildings in use [8]. In that work, the authors presented the definition, calculation methodology, and usefulness of these indices. The objective of that work was to provide indices as part of a complete and accurate method that does not require extensive information. The manner in which the building is managed, as well as the characteristics of construction are taken into account when computing energy qualification. This paper presents a methodology for the continuous assessment of energy efficiency in building in operation using the proposed indices. Results are obtained for 55 buildings, almost all of the Campus de Vera of the Universitat Politècnica de València (UPV), and for a period of an entire year. This enables an analysis of results for different building types and for different seasons under various thermal conditions. The paper is organised as follows. Section 2 describes the proposed methodology and the indices used. Section 3 presents the application and results of the methodology applied to many buildings and for an entire year. Section 4 presents reference values for commercial buildings and a discussion. Finally, some conclusions are drawn in Section 5.

2. Proposed index based methodology 2.1. Methodology steps

∗ Corresponding author. Tel.: +34 963 879 240; fax: +34 963 877 272. E-mail address: [email protected] (G. Escrivá-Escrivá). 0378-7788/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.enbuild.2012.01.020

The methodology of the continuous assessment of building in use is based on five steps (Fig. 1):

G. Escrivá-Escrivá et al. / Energy and Buildings 49 (2012) 78–84

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Fig. 1. Traditional audit and continuous energy assessment diagram.

1. Gathering of information: Site visit and collection of fundamental information for the analysis of a building. 2. Data acquisition of building energy consumption: Hourly consumption data is obtained at different electrical points. 3. Calculation of indices: The characterisation of the building is performed by calculating the proposed indices using the obtained information. 4. Analysis of results: By using the indices, malfunctions may be rapidly detected. 5. Proposal for actions: Actions to be performed to solve problems. Under continuous assessment these steps are performed each month. By using an energy management and control system (EMCS) developed at the Institute for Energy Engineering (IEE) of the UPV as a part of a project termed Distributed Energy Resources and Demand (DERD), it is possible to obtain the total electrical consumption of the university and of each building [9]. This study focuses on electrical consumption because electricity is the only energy source used in the buildings analysed. It is possible to apply the same methodology in other cases, with other primary sources, using as data the equivalent CO2 emissions of each energy supply instead of energy consumption [8]. 2.2. Gathering of information

chambers, etc.) because this determines the energy requirements. Hours of use. Energy demand depends on the hours of utilisation of the facilities, and a Boolean variable is established for each hour indicating whether the building is in use. Number of users. Each building has its characteristic total number of users determined by analysing each specific area and the percentage of users for each hour. Finally, the type of air-conditioning system. There are two common heating, ventilation, and air-conditioning systems (HVAC): centralised and individual (split systems). This parameter is taken into account to better analyse the results, and study the advantages and disadvantages of each type. - The considered environmental parameters are: external temperature. Temperature variation is one of the main reasons for monthly electrical demand variability. This parameter is acquired for each hour of every day during the studied months. Relationship between energy consumption and external temperature. Consumption in a building strongly depends on outside temperature [10]. Thus, a building has a higher consumption in months with extreme external temperatures such as February and July. To analyse the efficiency of buildings the parameter KT (T), T expressed in ◦ C, is introduced to consider the effect of temperature on energy consumption [8], defined as KT (T ) =

10

  T − 22 + 10

(1)

The parameters relevant for energy evaluation are presented below.

So by using KT (T) it is possible to normalise consumption during these extreme months.

- The dimensional parameters are: building space. This parameter is expressed in m2 and is the area included within the external walls of the building, except for outdoor space (terraces, etc.) or external access areas (stairs, elevators, etc.). Air-conditioned volume is the total volume of each enclosed area in a building that is air-conditioned (or heated) and is expressed in m3 . - The considered building characteristics are: building types. It is important to define the use of each building area (classrooms, offices, research labs, computer labs, halls, cold-storage

2.3. Consumption information The consumption information is based on hourly measurements. • Active energy (Wh). Overall energy consumption of the building is metered in the main breaker. • Air-conditioning active energy (Wh). In the studied buildings, the heating, ventilation, and air-conditioning (HVAC) system is

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supplied from a specific panel (CGAA) so that hourly energy consumption data can be measured. Generally, the HVAC system is the largest energy consuming facility in a building [10,11]. • Building-use energy parameter (Wh). This parameter is very important and must be carefully calculated. It is the equivalent energy constantly consumed in a building due to the facilities installed that cannot be disconnected, such as cold-storage chambers or servers. It strongly depends on the type of building and it is calculated for each month. 2.4. Performance indices Six partial indices and a global index were defined to identify and quantify inefficient energy consumption [8]. These indicators highlight excessive energy consumption due to the inadequate use of facilities or premises and which result in higher consumption in comparison with others. Furthermore, these indices enable comparison between different sized buildings with different numbers of users, and hours of use of the facilities [8]. In these indices, the values of energy consumption are the metered total consumption minus the building-use energy parameter, which is considered constant for each building and is calculated monthly. 2.4.1. ESH index Defined as ESH =

E(Wh) S(m2 ) · H(h)

(2)

where

2.4.4. ENU SHNU index Defined as ENU SHNU =

- ENU (Wh) is the energy consumption when the building is not in use. - HNU (h) is the number of no-use hours (when the building is not occupied). This factor determines the energy consumption when the building is unoccupied by considering the number of no-use hours. This is an important factor because it will be high if there is an incorrect use of the facilities during the night or holidays. 2.4.5. AEU VA HU index Defined as AEU VA HU =

In the buildings considered, air-conditioning systems represent the largest end-use consumption, and so it is worthwhile studying this end use in some depth [9,10].

where

Defined as KAEU VA HU =

(3)

where - EU (Wh) is the energy consumption when the building is in use. - HU (h) is the number of hours of use (when the building is occupied). This factor determines the energy consumption when the building is occupied. Thus, this factor measures the intensity of use. 2.4.3. EU UHU index Defined as EU UHU =

dU

E (kWh)/Ui (ud) i=1 Ui

(4)

HU (h)

where - EUi (kWh) is the energy consumption when the building is occupied. - dU is the number of occupied days in a month. - Ui (ud) is the average value of users during a day.



ERF =

dU i=1

AEUi (Wh) · KTi

(7)

VA (m3 ) · HU (h)

- AEUi (Wh) is the energy consumption in the air-conditioning system for a day. - KTi is calculated using the Expression (2) and the average value of Ti for a day of use. This is similar to the previous factor, but relates energy consumption to the external temperature. 2.4.7. Energy rating factor ERF The energy rating factor (ERF) is a global factor and can be defined by considering the mentioned partial indices. This index is calculated using the partial indices weighted according to their relevance, so the efficiency of a building is parameterised using various aspects. These weights penalise excessive consumption in hours when the building is not occupied, as well as in buildings with high consumption and few people, and building with inefficient HVAC systems. ERF is defined in Expression (8) where the values of the partial indices are normalised. For the normalisation, the average and typical deviation from each index of the monthly data collected is calculated. Each factor is normalised from 0 to 1 according to a normal distribution that obtains the value of the accumulative distribution for each factor.

100 − 10ESH + 10EU SHU + 25EU UHU + 25ENU SHNU + 5AEU VA HU + 25KAEU V A HU

This factor enables the detection of wasted energy when there are significant energy consumption and few users.

(6)

- AEU (Wh) is the energy consumed by the air-conditioning system during a month. - VA (m3 ) is the air-conditioned volume of the building.

This factor is used in most energy efficiency research and indicates the overall building energy consumption.

EU (Wh) S(m2 ) · HU (h)

AEU (Wh) VA (m3 ) · HU (h)

where

2.4.6. KAEU VA HU index

Defined as EU SHU =

(5)

where

- E(Wh) is the overall energy consumption during a month. - S(m2 ) is the building surface. - H(h) hours in a month.

2.4.2. EU SHU index

ENU (Wh) S(m2 ) · HNU (h)

10



(8)

Low ERF values show that the building analysed is less energy efficient and values closer to 10 reveal the more energy efficient buildings.

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3. Application and results of the proposed methodology In the UPV there are about 65 buildings, some 40,000 students, 3000 lecturers, and more than 2500 administrative staff. During the implementation of the DERD project in the UPV more than 250 power meters were installed and more than 2000 lines were controlled. Many of these lines were associated with the most energy-intensive processes, such as HVAC systems, extractors, and lighting circuits [12]. The methodology presented in this paper has been applied to continuously assess energy efficiency and rank 55 buildings on the UPV campus. For the continuous assessment of energy efficiency, the calculation of the indices is made at the end of each month. The necessary information for the calculation is: - Hourly energy consumption for the period, directly obtained using the EMCS. - Hourly external temperature for the month. Data are obtained either by measurements through the EMCS by means of a sensor installed on a rooftop in the UPV, or using a weather database website [13]. - Number of users of the building. For each hour of the day, a percentage value is assigned that reflects the number of the building’s users that are in the building – depending on the day of the month. - Number of hours of use. This value reflects the number of hours that the building is in use and depends on whether it is a weekday, weekend, holiday, etc. - Building-energy use parameter. There are periods in which this parameter may change depending on the activity. Number of users, hours of use, and the building-energy use parameter are calculated at the end of the month by the building energy office at the IEE (responsible for permanently analysing the behaviour of each building). Therefore, by automatically using the EMCS a value is obtained of all the partial factors and the ERF, and this enables a ranking of the analysed buildings to be produced. This paper presents the obtained results from November 2009 to October 2010. Table 1 presents the analysed buildings and the main characteristics obtained in the energy audit. Many different types of buildings have been analysed with respect to size, type of HVAC system, use, etc. The partial indices and the ERF are calculated for each month. As example, results for analysed buildings for July 2010 are also shown in Table 1. The common index ESH is unable to accurately reveal energy efficiency. In contrast, the ERF index takes more information into account. Single-use buildings and buildings with HVAC systems based on individual units are more highly ranked. In buildings 6C and 5G, the ESH values are similar to EU SHU values (e.g. 50.79 and 62.25 W/m2 respectively, for building 6C). This reveals that the HVAC system remained on during the nights. This malfunctioning is also detected in the high values for index ENU SHNU for buildings 6C and 5G (33.66 W/m2 for building 6C and 35.55 W/m2 for building 5G). However, the HVAC is well designed as the AEU VA HU index presents low values (6.20 W/m3 for building 6C and 3.81 W/m3 for building 5G). Building 5O shows a very low value for ESH, but a medium ERF value. This is because consumption has not been adapted to the number of users, and so there are high values for EU SHU . Moreover, moderate consumption during the nights (medium value for ENU SHNU ) can be seen. As an example of the continuous evaluation, results for building 8G8E are shown in Table 2 for the analysed period. Changes in the indices along the year may be appreciated. Generally, EU UHU increases during the holiday month of August (for example, 3.05 W/m3 for building 8G8E) due to the HVAC systems not being

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adapted properly to the number of users. KAEU VA HU is more stable in buildings with a well designed HVAC system than AEU VA HU because this index takes into account that extreme temperatures require more energy consumption in air-conditioning. ENU SHNU presents high values in building 8G8E and reveals that some facilities should be switched off during the nights. Fig. 2 presents the ranking of 55 analysed buildings using the ERF for the studied months. Significant changes in a building indicate that an anomalous situation occurs. Three buildings (8K, 1A1B and 8G8E) are highlighted in the figure. It can be observed that buildings normally show a steady behaviour, except for those buildings that had a specific period of abnormal behaviour. Using ERF, building 8K is seen to behave well and is characterised as a good consumer. On the contrary, building 8G8E shows not only high ESH values but also high ERF values. This is explained by the inefficiency of its airconditioning system. Finally, building 1A1B has a steady behaviour except in the winter months, and this is due to the common use of electric heaters. Significant changes in ERF or in any of the partial factors of a building may be an alert for malfunctioning, as occurs in building 5G during July to September 2010. September is the month with the highest ERF, and this indicates a correct use of energy during this period due to moderate external temperatures and high occupancy of the buildings. Centralised air-conditioning buildings generally have a lower ERF than buildings with individual air-conditioning systems and single-use buildings. This is because the nature of these buildings enables a proper schedule of the facilities to be established [14]. In spring and autumn some buildings have significant changes in ERF because air-conditioning systems are less used.

4. Reference value analysis In Table 3 values for the partial indices for the entire period analysed are shown. Pk represents the percentile k and Pk is the threshold level that K% of all the values of the sample are below (then 100 k% are higher). In this work are fixed P20 and P90 as significant parameters considering the values taken by the proposed partial indices. Building with indices below P20 may be considered as excellent in energy consumption terms. Buildings with indices higher than P90 may be considered as poor in energy consumption terms. As the average value of ENU SHNU is 2.99 W/m2 this means that generally every building has a residual consumption. Evolution during the year is also analysed in Table 3. It is shown that ENU SHNU generally remains stable during the four seasons. This indicates that the building-use energy parameter has been correctly defined for the analysed buildings. Months with high energy demands due to the extreme external temperatures present higher values in the partial indices. EU UHU increases significantly in summer (1.14 kW/ud, 267% higher than in autumn). This indicates that large quantities of energy, more than in winter months, are used to satisfy the summer air-conditioning requirements. Individual air-conditioning systems present indices 50% lower than HVAC centralised systems. This reflects the importance of a correct management of the centralised systems to improve energy use. This methodology facilitates a continuous audit of the buildings in use and a rapid reaction to failure. It is also possible to make a comparison between buildings and identify a correct design of the facilities (mainly the HVAC system). Values presented in Table 3 may be used as a reference for other commercial buildings in use, taking into account the periods of the year and the type of air-conditioning system. With these values an absolute characterisation of similar buildings may be made.

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Table 1 Analysed buildings, partial indices and ERF for July 2010. Ranking

Building

Use

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55

8K 3C 3B 4I 5C5D 2A2C 4A 8H 2B 3I3J 3G3H 6E 4G 2F32F4 9A 3F 8L 5L 4H 1A1B 5J 5M 4D 4E 5O 5I 5N 1G 1E1H 1C 8A8C 5F 5H 6A 6D 7D 2D 7B7E 5E 3K 4K 5K 7C 7A 7F 7I7J 5G 6G 1F 7G 2E3A 6C 4L 9C 8G8E

O/C O/C O/C O/C O/C O/C O/C O/C O/C O/C O/C O O/C O/C S O/L O O/C O/C O/C O/C O/L O/C O/C O O/L C O/C O/C O/C L O/C O/C S O/L O/L O/C O/C O/C O/L A O/L S O/C O/L O/C O/L O O O/L O O/L R O/L O/L

Area (m2 ) 4192 3271 2592 3267 5590 12,397 3282 4111 4691 5679 6146 1095 9541 8170 2094 2603 1194 1135 3474 18,717 3362 4764 10,380 3398 5112 798 5160 10,170 7980 6124 713 3724 3364 5875 6287 3663 3429 19,768 9651 3365 5840 2090 2684 3489 2833 21,026 1960 8595 4819 3377 8544 6510 9282 7323 34,068

Conditioned volume (m3 ) 10,248 7770 6165 8490 13,293 29,304 7635 7009 11,103 10,257 13,898 2739 14,478 19,266 2168 5334 1099 2604 8394 40,482 6528 12,399 23,085 7482 2169 1950 9435 20,643 16,783 15,033 1656 8778 6440 1290 13,128 10,227 6837 47,190 15,353 6038 9439 4605 8995 6333 5376 31,736 4971 22,465 8313 7620 16,239 14,151 29,612 14,019 89,265

No. users

Building-use energy (kWh)

HVAC system

ESH

EU SHU

ENU SHNU

AEU VA HU

ERF

173 268 279 295 433 473 199 140 417 275 530 61 535 650 35 161 13 125 170 1639 198 259 989 228 15 45 475 912 554 256 30 280 208 13 268 214 185 1196 181 130 547 198 150 217 113 538 197 333 418 219 159 378 388 300 1195

4 4 3 2 12 7 4 0.8 2 10 10 1 15 3 2 6.9 0.4 2 5 35 1.4 20 45 8 5 4 5 100 15 2 14 1.2 3 1 15 1 7 20 15 13 17 10 1.8 2 12 15 2 23 40 10 50 180 145 40 700

In In In Both In In Both In In In In In In In In In In In Both In Both In In Both In In Ce In Ce Ce Both Both Both In Ce Ce In Ce In Both Ce In Ce Ce Ce Ce Ce Ce Ce Ce Ce Ce Ce Ce Ce

3.72 5.03 2.60 4.54 4.92 4.13 5.36 4.75 5.11 5.31 7.65 7.94 4.44 5.82 4.30 6.64 3.21 10.38 9.54 10.05 8.48 10.40 9.43 11.16 2.33 10.89 8.45 10.13 9.29 8.76 12.40 10.58 9.26 2.67 7.43 11.74 10.19 11.41 7.58 13.55 14.89 18.59 14.60 12.42 11.69 11.01 37.57 17.63 19.31 21.19 20.54 50.79 32.72 22.38 29.05

6.31 9.03 4.66 8.19 8.67 7.48 9.38 7.97 9.24 8.38 13.18 14.49 7.9 10.73 6.84 9.49 5.54 17.11 16.83 15.26 15.05 17.1 15.7 18.34 2.87 18.81 15.62 16.07 17.6 14.93 21.00 17.46 16.09 2.90 12.40 20.87 17.99 18.95 12.68 20.34 19.48 32.23 25.53 21.96 20.52 20.67 42.68 26.48 33.16 35.95 36.99 62.25 36.99 31.87 46.48

1.08 0.83 0.32 0.59 1.15 0.73 1.05 1.43 1.16 2.33 1.94 0.79 0.81 0.91 1.51 3.71 0.65 3.27 1.79 4.88 1.83 3.92 3.71 3.67 2.02 2.69 1.69 4.72 1.11 2.33 3.70 3.76 2.66 2.12 2.32 3.69 1.88 4.74 2.43 7.31 2.97 5.38 3.54 3.94 4.07 1.95 35.55 6.49 6.29 7.84 4.39 33.66 11.37 13.53 12.81

0.90 1.03 3.38 2.90 2.45 2.29 2.28 1.50 2.95 1.99 1.94 1.55 3.92 3.83 2.08 2.83 2.10 2.49 2.60 2.40 4.40 2.35 4.26 3.04 0.78 3.18 5.93 4.63 5.96 5.08 1.84 5.09 5.70 2.59 6.68 5.68 7.70 7.00 6.97 4.00 9.52 5.08 6.80 8.64 8.62 10.84 3.81 7.70 13.62 10.73 17.91 6.20 10.53 14.80 14.08

7.92 7.91 7.73 7.62 7.57 7.56 7.53 7.52 7.44 7.39 7.28 7.25 7.22 7.11 6.97 6.96 6.91 6.72 6.53 6.52 6.31 6.26 6.21 6.17 6.14 6.13 6.01 5.88 5.81 5.80 5.73 5.71 5.70 5.64 5.61 5.00 4.99 4.65 4.5 4.49 4.45 4.43 4.20 4.10 3.72 3.11 3.10 2.89 2.62 2.02 1.48 1.31 0.96 0.88 0.33

Note: O: Offices, L: Laboratories, S: Sport facilities, R: Library, A: Study building, Ce: Centralised, In: Individual.

Table 2 Results for building 8G8E for November 2009 to October 2010. Index

Nov 09

Dec09

Jan 10

Feb 10

Mar 10

Apr 10

May 10

Jun 10

Jul 10

Aug 10

Sep 10

Oct 10

ESH EU SHU EU UHU ENU SHNU AEU VA HU KAEU VA HU ERF Ranking

21.0 31.7 1.37 11.3 9.09 6.08 0.08 53

15.0 26.3 1.18 6.42 7.74 4.23 0.60 53

15.3 26.5 1.18 7.29 7.55 3.50 0.43 55

17.5 28.3 1.23 7.03 7.69 3.84 0.39 55

17.0 27.8 1.19 6.59 7.56 4.01 0.21 55

17.1 29.2 1.26 7.72 8.23 5.30 0.16 55

20.6 33.8 1.45 9.29 9.79 6.83 0.09 55

22.5 36.3 1.55 8.72 10.7 8.36 0.05 55

29.0 46.5 2.05 12.8 14.1 9.74 0.33 55

21.5 33.1 3.05 10.7 10.8 7.57 1.15 54

20.6 33.0 1.41 8.05 9.77 7.58 0.36 55

16.9 28.2 1.22 7.55 7.75 5.43 0.10 55

G. Escrivá-Escrivá et al. / Energy and Buildings 49 (2012) 78–84

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Fig. 2. Ranking using ERF index for buildings analysed from November 09 to October 10.

Table 3 Values for partial indices. Analysis in function of the season of the year and type of air-conditioning system. Dec., Jan., Feb.

Mar., Apr., May

Jun., Jul., Aug.

Sep., Oct., Nov.

Centralised/both

Individual

Index

Avg

Nov. 09 – Oct. 10 P20

P90

Avg

P90

Avg

P90

Avg

P90

Avg

P90

Avg

P90

Avg

P90

ESH EU SHU EU UHU ENU SHNU AEU VA HU KAEU VA HU

7.81 12.77 0.61 2.99 3.26 2.09

3.70 6.27 0.18 1.01 1.33 0.10

13.60 21.50 1.27 6.20 6.77 4.55

8.14 13.91 0.49 3.08 3.35 1.63

12.68 21.43 0.90 6.09 6.29 3.19

6.80 11.06 0.39 2.82 2.58 1.57

11.02 17.52 0.86 6.06 5.46 3.31

8.75 13.88 1.14 3.41 4.09 2.95

17.25 28.91 3.04 6.90 9.27 6.66

7.57 12.23 0.43 2.66 3.03 2.22

12.97 21.08 0.85 5.50 6.46 4.64

10.20 16.31 0.73 4.07 4.40 2.85

18.02 27.88 1.35 8.56 8.63 5.43

5.13 8.78 0.48 1.78 1.98 1.24

8.66 14.71 0.93 3.48 3.45 2.16

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5. Conclusion Energy efficiency is now a hot topic. Traditional energy audits are performed to improve energy efficiency in buildings. However, the traditional audit has a major drawback – namely, that it measures performance at a given moment in time. This article presents a methodology for the continuous characterisation of buildings in use by using energy rating factors. This new method facilitates the detection of abnormal situations and enables rapid action to be taken to improve energy use in buildings. The methodology was applied to 55 buildings at the Universitat Politècnica de València campus during an entire year. Reference values of the different factors were obtained for types of buildings and this enables the characterisation of similar buildings. Furthermore, the study has shown that, in general, single-use buildings with individual air-conditioning systems have a better energy performance than other buildings with centralised HVAC systems. Acknowledgments This research work has been possible with the support of the Universitat Politècnica de València through grant CE19990032. References [1] A. Ghoshray, B. Johnson, Trends in world energy prices, Energy Economics 32 (5) (2010) 1147–1156.

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