UNIVERSITÀ DI PISA
Scuola di Ingegneria
Corso di laurea in Ingegneria Energetica
Tesi di Laurea Magistrale
Methods for energy performance analysis of university buildings through
measures of environmental parameters
Relatore
Prof. Alessandro Franco
Prof. Francesco Leccese
Candidato
Lorenzo Marchi
1. Introduction 4
2. Energy Efficiency of Public Buildings 9
3. CO2 as a mean to analyse the buildings’ occupancy 50
4. CO2 Measurements in real buildings 61
4.1 Sites’ clustering and classification 61
4.2 Monitoring procedure 71
4.3 Data Elaboration and indicators 78
5. Data analysis and processing 81
5.1 Data analysis 81
5.2 Data processing 105
6. Energy efficiency observations through CO2 monitoring experiences 116
7. Conclusions 123
Bibliography 126
Appendix 1 129
Appendix 2 159
1. Introduction
A very sensitive area whose design has been deeply affected by the latest worldwide energy policies is the building and construction sector which accounts for 36% of global final energy consumption and nearly 40% of total direct and indirect CO2 emissions [1].
Although the energy use in building is projected to grow by 32% between 2015 and 2040, its global energy consumptions’ share is to remain the same [2].
Buildings’ total energy use is driven by both improved access to energy and rising of the standards of living in developing countries, where energy efficiency policies are still to be properly
introduced.
In order to increase energy efficiency in the building sector there are two main strategies [3] :
• Technology improvements such as switching between types of water heaters, adopting the led technology for illumination and implementing intelligent energy system management. • Creating energy efficiency policies as it is happening in most countries
Great importance has been given to “passive building design” trying to build a more efficient envelope in order to minimize the need of electricity or heat by the energy system.
Some common practices are the so called “zero-energy building” or “nearly-zero-energy building” which attempt, integrating the building envelope with proper energy generation systems (for example photovoltaic panels or geothermal heat pumps) and using particular building materials in order to make the building envelope insulated from the outside when needed (for example by using Trombe walls), to make the building self-sustaining under an energetic point of view and so
decreasing the need of burning Greenhouse gas emitting fuels [4].
The implementation of such practices in public buildings is more intricate: this sector is
characterized by occupancy schedules that varies widely during the year and that are not easily forecastable, by environments initially designed for a specific purpose that have been subsequently readapted and, generally, by large volumes.
Not only energy efficiency is important but, because these building are often highly crowded, comfort is a prime importance factor.
A group of people within a closed environment inevitably influence the air composition,
temperature and chemistry [5], affecting the thermal and air quality comfort of the environment. Each of the occupants influence the air compositions by:
• Heat generation: the metabolic rate of the human body causes heat losses and
consequently an environment’s temperature increase. The human body heat emission rate depends on the activity performed, by the age of the individual, the daytime and several other factors.
• Humidity: the human body emits vapours through breathing, that inevitably mixes with the air.
• CO2 and VOC: A waste product of the human breathing is the Carbon Dioxide (CO2) and several Volatile Organic Compounds whose concentration in the air could trigger some diseases and affect productivity. The amount of pollutants emitted by the human body is difficult to forecast, depending on several factors
Thermal and air composition comfort are often in contrast with energy efficiency [6], given the fact that the only remedy is the systematic use of air conditioning system that brings great energy consumptions.
For these reason, the implementation of Zeb and n-Zeb in the public building sector is still a distant goal.
The opposite approach with respect to Zeb buildings is the design of a completely sealed environment which air characteristics are completely up to the air-conditioning system. Although this design provides a good comfort monitoring for the environment, the absence of natural air exchange brings to elevated energy consumptions.
There are other factors that influence energy consumptions:
• Building’s envelope: public buildings sector is composed by a mix of old, refurbished and new buildings, each with its characteristics that affect the energy consumptions and, consequently, energy efficiency.
• Climate
• Inner electrical consumes (plug-in devices, lightning devices, cooking devices etc.) • End-use
Occupancy is a key factor in public buildings’ energy consumptions and its detection is very helpful in assessing the energy efficiency. For that purpose, many techniques have been proposed, each one with its advantages and disadvantages [7].
Currently, the occupancy sensor market is dominated by infrared and ultrasonic detectors. These relatively simple devices provide fine-grained information on peoples’ presence and location. Their output is binary, it detects the state “occupied” vs. “not occupied”.
The simplest way to obtain information regarding the level of room occupancy is to employ more advanced, especially dedicated equipment, such as vision sensors for example video cameras and RFID (Radio Frequency IDentification) tags.
Video cameras are a reliable and accurate way to determine not only if a space is occupied, but also the number of occupants and their identities. However, counting people directly may be challenging due to partial/complete object occlusion.
Main drawbacks of this technology are: the need of large data storage capacity, the fact that
commercially available software-based image analysis for application to building system control is still under development and, above all, privacy concerns. That is why several works using low-resolution camerasor even developing “reduced” sensor from camera, with a very different appearance from conventional video camera, have been proposed to obtain enough information to detect person’s position and movement status, while reducing the psychological resistance against having a picture taken.
Electromagnetic signal detection systems, radio frequency identification have the capacity to provide occupancy information on location, number of occupants, their identities and track. RFID is an effective technology for indoor localization that can provide adequate accuracy and fine-grained occupancy information. It is cost-efficient and does not require line of sight conditions. In addition, it has on-board data storage capacity that can be used for another purpose such as building asset management. The disadvantages of this technology result from the privacy
(psychological resistance of the tag users), technical problems and some inconvenience. Occupants must possess the RFID tag. Therefore, users feel they are constantly being monitored. Frequently, occupants use more than one device generating EM (electromagnetic) signals, which can be detected by the radio frequency identification system, and results in false registration of presence and count as well as incorrect location. The terminal device held by the user is usually battery powered and not sustainable for long-term data acquisition, a complex and advanced signal processing station is often required and, moreover, the fact that the RFID tag must be attached to the occupant causes some inconveniences for the user performing everyday activities.
The drawbacks of the direct determination of occupant number make this approach unsuitable in certain situations.
Therefore, indirect inference of occupancy levels in rooms or buildings is considered. This alternative approach exploits signals originating from different buildings systems and indoor environment.
Recently, it was proposed to estimate the number of indoor occupants from environmental parameters, in particular such as temperature, humidity, and CO2 concentration. Amongst these parameters, CO2 concentration is the one that most correlates with the number of occupants. As mentioned above, humans naturally exhale this gas depending on their metabolic rate. Hence the analysis of the dynamics of CO2 concentration may be employed in determining the number of occupants inside a room.
Moreover, the measurement of CO2 is useful not only for the occupancy detection but, since CO2 is a gas which, after prolonged exposure, can cause undesired side effects on the occupants, also for comfort purposes.
Implementing CO2 sensors into the air handling unit could bring to a double benefit, that is occupancy definition and comfort assessment.
Considering the fact that CO2 sensors are little and do not represent a disturbance of any kind to the unfolding of activities within the rooms in which they are installed, the developing of techniques of occupancy definition using CO2 concentration represent a valid and effective way.
Though if the matter of energy efficiency and comfort is open in various public buildings categories (hospitals, museums, commercial centres etc.) it is more intricate in the university buildings sector which is characterized by:
• Buildings with different characteristics: universities are composed by old, new and refurbished building and inside them are present various end-use areas like labs, lecture rooms, cafeterias, dormitories, sport facilities, medical structures etc.
• Energy consumes not easily forecastable: especially in universities where attendance is not mandatory, the occupancy and its related consumed varies widely both with the day of the year (exam period or lecture period) and the hour of the day.
• Energy systems: the energy systems are rarely provided with proper monitoring and controlling systems causing them to work in conditions far from optimal.
In this sector it is possible to understand how in the conventional design protocol (based on generic conditions) the concept of energy efficiency, implemented exclusively with respect to the building’s characteristics, has no real meaning.
In order to conduct an effective analysis, it is necessary to be properly aware of the building operative schedule variations and consequently its real occupancy.
The main aims of the thesis are to:
• Show the limits of the ‘’static’’ approach (certification).
• Try to establish a link between the real building utilization and the effective energy consumptions adopting the occupancy as ‘’base element’’.
• Evaluate the CO2 monitoring as an estimate element for the building utilization and its energy system feedback.
A CO2 monitoring activity have been performed in order to meet the previously mentioned goal, and the analysis has been conducted with the following structure:
First an analysis of the literature related to the public buildings’ energy efficiency assessment and of previous researches regarding CO2 as a mean to detect and forecast buildings’ occupancy has been done in order to give a solid and factual contextualization of the issue.
Then the monitoring activity is exposed: the buildings characteristics, the site’s identification, the campaigns’ planning, the instruments’ description and the monitoring protocol
Followingly, the monitoring data are exposed and processed, and then they have been used to make some observations regarding the energy efficiency of the buildings under analysis and their
systems’ feedback.
2. Energy Efficiency of Public Buildings
In this chapter will be analysed some energy efficiency assessment of the public building sector, which is usually done through the benchmarking procedure:
The most important parameter to assess energy efficiency is the building’s energy consumption. For this reason, will be analysed the main methods to gather consumption data:
1. Through energy bills 2. Through energy metering
3. Through energy consumptions forecasting
The third data source will be further studied, analysing the various computer software’s and some procedures to reduce the number of data required.
Afterwards, will be analysed the presence of a so-called ‘’performance gap’’ between forecasted and actual consumption data and its causes.
For a complete energy efficiency assessment, it is necessary to consider other parameters such as building typology, end-use, deviations from the standard climate conditions and the building’s operational management.
These parameters will be analysed and will be illustrated some methods to keep their effect in consideration.
The benchmarking procedure can be applied on different design levels of the building under analysis.
It’s possible to identify three levels of design within which it’s possible to intervene with a benchmarking assessment (Figure 2.1)
• Synthesis: it’s the first stage of the design procedure, during which general assumptions are made. During this phase no bond are present, the designer can opt for the solution he prefers and for that reason the energy efficiency characterization is more severe. This is the so-called “green field”.
• System: Once the first stage has set certain bond regarding the main quantities of the building, the energy system is designed. The design at this stage considers exclusively the standard data of the system chosen and set its size related to building typology and standard climatic and occupational data. The design of a system onto a pre-built site could cause inevitable energy inefficiencies to be present, such assumption must be considered during this phase’s energy efficiency assessments.
Synthesis System Operative Design 1st level Variables 2nd level Variables 3rd level Variables
• Operative: It’s the last stage of the design once the building and energy system has already been dimensioned. The design at this phase considers how the building and its energy
system (considered as a whole) behave during specific occupational and climatic conditions.
Every energy benchmarking procedure, in order to be effective, needs to quantify the behaviour of a certain building compared to a building stock of the same kind.
To be able to do so, a proper database needs to be built and many data needs to be gathered:
• General information about the buildings: Address, name etc.
• Structural information: category of building, gross heated volume, building envelope composition etc.
• Climatic information: Climatic area, HDD, building orientation etc. • Energy system information: type of energy system, schedule, efficiency • Consumption information: Gas consumption, Electricity consumption
There are generally 3 methods to gather data regarding energy consumption of certain buildings, which are:
• Energy Bills reading • Energy metering
• Energy consumption forecasting through software simulation
Energy bills are the most immediate source of data because every energy system is endowed with a counter (electricity or fuel) that measures the energy supplied.
Successively assuming the energy system efficiency is possible to estimate the effective building’s energy demand.
The weakness of this method is that for the current regulations, only one meter for a whole building or worse for a whole building stock is mandatory, therefore it’s difficult if not impossible to isolate the consumption of a specific area.
Moreover, it’s impossible to obtain the effective building’s energy demand without assuming a standard efficiency of the energy system which value could differ consistently from the real one.
For those reasons nowadays, most tertiary buildings of new construction, or undergoing radical refurbishing, are now being equipped with sophisticated and powerful computer-based Building
Management Systems (BMS), which monitors and controls mechanical and electrical equipment such as lighting, power supply, fire prevention, security, and HVAC.
BMS can effectively perform energy metering (fuel consumption, electrical energy input to specific components, delivered energy to fluid networks), provided the energy metering functions are clearly indicated among the design specifications of the BMS in terms of installed instrumentation (electricity meters, temperature sensors, fluid flow meters, etc.).
An example of such method of data gathering for a university buildings’ energy efficiency evaluation is given by Sretenovic [8]:
In this thesis have been analysed the energy use at NTNU campus Gloshaugen, consisting of 35 buildings with total area of approximately 300,000 m2. It includes the Faculties of Engineering Science and Technology, Natural Science and Technology and Information Technology, Mathematics and Electrical Engineering.
Due to different building use and year of construction have been used various materials.
There are 46 heating meters and 79 electricity meters installed but the bills for heating for campus are defined by the main meter, that is installed by the district heating supplier.
The NTNU has installed its own main control heating meter, which is located in the Old Electric Building as showed in the figure (Figure 2.3).
The main meter for electricity use, which measurements are relevant for billings, is installed by the electricity supplier.
The campus is provided with BEMS (building energy management system) and web-based Energy Monitoring System (ERM), which receives main and sub-meter consumption data and provides system energy reporting, alarming, monitoring and analysis.
Figure 2.3 – District heating and satellite view of the campus
District heating net in the university campus is shown in Figure 2.3.
The supply is organized in the shape of a ring and the main heat exchanger is installed in building Old Electric Building.
Thanks to the meters and sub-meters is possible to obtain a vivid depiction of the actual consumption of both electricity and Fuel for each day, month or year.
An example is given in Figures 2.4 and 2.5 where are reported the energy consumption during a typical day.
Figure 2.5 – Hourly electricity energy consumption
This approach makes it possible to give accurate statements regarding energy consumption of a certain building or building stock, even if the installation of said instrumentation must be done with great care in order not to obtain redundant results.
One advantage of said method is the possibility to set the frequency of the measurements to obtain a higher precision. Often said frequency is set from 15 minutes to an hour and considering that energy bills shows only monthly consumption is obvious how this method can value certain daily
phenomena otherwise not evaluable.
The third approach, namely the simulation through computer software, is widely used especially while inquiring possible refurbishments or while erecting new buildings whose energy consumption otherwise couldn’t be measured directly through bills and/or the installation of energy meters. Computer simulation software are of two main categories: physical based and black-box methods.
Physical based methods are characterized by the use of software that describe the building under analysis using multiples physical variables such as inner and outer temperature, inner and outer humidity, walls composition, thermal resistance, U-values, sun radiation, air tightness etc. Such descriptions need a great amount of data and for that reason the simulation is often complicated, expensive and time consuming.
An example of such method is given by Allab et al. [9]: the studied site was built in 1987 with a total net floor surface of 30580m2. The site consists of a central hall, six buildings of 5 floors each, and a gym. The six buildings are all connected to the central hall, as well as the gym (Figure 2.6)
Figure 2.6 – Satellite view of the study’s site
The building indoor environment is controlled by two energy systems: the air handling unit and a heating floor system. Both systems use hot water as the heat transfer fluid.
The envelope of the building is characterized by the mixed employment of concrete, windows and aluminium supports. Large windows are generally designed especially to the southern side. Natural lighting is a trivial advantage of this design even if it induces large heat losses through cold bridges. A building energy model (BEM) of the case study was implemented under TrnSys 17.
A representative sample of the campus, with respect to uses and occupation levels, was chosen to develop the model.
The considered sample is an intermediate level, the 2nd floor, of building #2 for instance. The sample was instrumented with ambient temperature, relative humidity and Carbon dioxide (CO2) sensors in each room together with the fluid’s temperature and flow rate in the HVAC system during a four weeks measurement campaign.
Occupancy and equipment’s heat gain were also taken into account.
The characterization of the climatic conditions was made simultaneously to ventilation measurements by means of a weather station distant 20 km from the studied building (air
temperature, relative humidity, speed and wind direction). Outdoor CO2 rates were recorded by a data logger on the site. The indoor climatic conditions in the occupied zone and in the zone of interest (operative temperature, relative humidity, air velocity) were measured by an indoor climate station.
The carbon dioxide sensors are used to establish the air tightness and the air exchange rates of the case study.
The building’s walls composition was known from the building plans, U-value was determined through simultaneous three temperature measurements at the wall inner surface, one temperature measurement at the wall outer surface and indoor and outdoor temperature measurement.
The heat flux is then calculated between the outer surface and the outdoor temperature using a conventional convection/radiation heat exchange coefficient.
The flux value is then used to deduce the U-value from the temperature gradient though the wall. Once the building heat loss coefficient is determined, the building’s thermal mass was determined by identification to match experimental data of the building temperature variation without internal heat gains. For that reason, the HVAC systems were shut down during three days of zero occupancy to reduce the number of unknown parameters.
Finally, all these values are used in the simulations.
The developed BEMwas used to test several scenarios implementing different improvements of the building envelope and/or the HVAC system. The model is used to quantify the impact of each scenario on the building energy consumption and energy cost compared to the current
configuration. The scenarios and results presented are obtained under the constraint of an indoor temperature of 20 ◦ C.
The considered scenarios are:
1. The use of a heating curve lower than the current one;
2. The optimization of the ventilation system scheduling such as its overall functioning duration is reduced while the set point temperature is always reached at 8 A.M;
3. The implementation of a control system on the heating floor which takes into account the indoor temperature as a feedback to reach the set point temperature;
4. In addition to a lower heating curve, an additional insulation of the envelope (a seven-mm thick stone wool layer) is considered;
The thermal energy consumption and cost of the building #2 for each scenario as well as for the original situation are presented (figure 2.7)according to an increasing implementation difficulty scale from left to right.
Figure 2.7 – Consumption scenarios forecasting vs actual consumption
Even if this approach is able to forecast possible refurbishment and new buildings’ energy
consumptions, it requires a large amount of data. This fact makes this approach expensive and time consuming.
An attempt to decrease the variables needed to characterize a building energy behaviour during a physical based computer simulation is proposed by Mao et al [10]:
EnergyPlusis employed as the simulation tool in the study because it can provide the capability to simulate a wide range of building design features and energy conservation measures. Although EnergyPlus can perform energy modelling with a good accuracy, it is quite complex and error-prone to conduct such a large number of simulations due to the huge amount of data to be analysed. A base-case office building is created to serve as a baseline reference, which is of great importance since all the subsequent calculations and analyses are performed based on it. The established base-case model is a 12-storey office building (40 m × 40 m) with the curtain-wall design and a
centralized HVAC system. The floor-to-floor height is 4m, and the gross floor area is 19,200m2. The air-conditioning design is set as fan coil system with five air-conditioned zones, four at the perimeter and one interior (20m×20m). The building and HVAC systems operate on a 10h day (08:00–18:00) and a 5-day week basis.
A summary of key input parameters is shown in the figure below (Figure 2.8):
Figure 2.8 – Key input parameters and procedure scheme
Apart from the building description, the external weather data is another vital factor in the simulations.
In order to reduce the number of variables needed in the model, a sensitivity analysis is set up. Sensitivity analysis is the study of whether and how the output of a system is influenced by
different inputs, it offers to the researchers some insights as to what is important and what is not in a specific system.
A list of input variables, which represents a variety of different factors encountered in the building design, should be prepared to enhance the value of the pre-simulated database. These are the
Figure 2.9 – Influencing factors and numerical perturbation level
Perturbations are introduced by assigning a range of different numerical levels to each input parameters for the subsequent experimental design.
The following step of the sensitivity analysis consists in the orthogonal experimental design (OED), that is a commonly used experiment design method. It selects representative data points from full factorial design in a way that these points are distributed evenly within the test range. Thus, the primary virtue of OED is that it uses only a fraction of the runs needed for full factorial design, while still yields good effect estimates (small bias and high precision). OED is usually developed based on the orthogonal array (figure 2.10) which makes the design fast, efficient and economical.
Figure 2.10 – Orthogonal array
The 13 parameters are selected as the control factors labelled as A–M (figure 2.10). Each factor has three levels represented by digits 1–3. The orthogonal array L27 (313 )is used for the design: the 27
indicates 27 trials, the 3 indicates the number of levels and the 13 indicates the maximum number of factors. The 13 control factors are assigned to the columns, and the 27 trials are allocated to the rows. Each level is repeated the same number of times (9) in each column. For each trial, the annual building electricity consumption in kWh/m2 is calculated using EnergyPlus (AEC).
Analysis of variance (ANOVA), which can largely reduce the number of required experiments and can achieve good results, is one of the most versatile statistical methods. The variance analysis and F-testare performed to distinguish the effect of the factor from the fluctuation errors and to assess the statistical significance of each factor. The influencing factor with a larger variance value is indicated to have a greater impact on the system performance.
Also, the significance of the influencing factor can be judged by comparing the F-test value and critical F value. Meanwhile, the contribution rate of each influencing factor is introduced to evaluate the relative importance. It is defined as the percentage ratio between the variance of each influencing factor and the sum of all the influencing factor variance.
The F-test values of the summer set point temperature (F), occupant density (H), lighting load density (I), equipment load density (J), chiller COP (L) and pump efficiency (M) are greater than F0.01(2,2). This indicates that these six factors are highly significant to the annual building
electricity consumption. Besides, the window-to-wall ratio (B), window U-value (D), shading coefficient of window (E) and fan efficiency (K) are found to be relatively significant. In contrast, the building orientation (A), wall U-value (C) and winter set point temperature (G) tend to exert nearly no impact on the annual building electricity use.
In general, a total of 10 key design parameters are identified: window-to-wall ratio, window U-value, shading coefficient of window, summer set point temperature, occupant density, lighting load density, equipment load density, fan efficiency, chiller COP, and pump efficiency. These 10
parameters are thus selected as the input variables of prediction model.
The result of the simulation shows a relative error less than 8.5% respectively compared with the measured values. In addition, the error is positive, indicating that the developed SVR model tends to overestimate the annual electricity use. Generally, the deviations are acceptable (within 10%). The predicted values can follow quite closely those from the measurements.
It is thus possible reduce the number of input variables needed while simulating the energy consumption of a building achieving good results and making the simulation less cost-effective, faster and simpler.
Even if is possible to reduce the requested input data for the physical based methods, the black box methods require a still lower number and, for that reason, results simpler and less cost-effective. As explained by Manfren et al. [11] black-box models are empirical or data-driven models based on little or no physical behaviour of the system and rely on the available data to identify the model structure.
They are suitable for predicting future behaviour under a similar set of conditions and are
characterized by computational efficiency and flexibility, simple implementation with respect to the achievable accuracy but the absence of a physical representation makes them opaque to the user. The model is represented as the operator 𝐺(∙) that associates input variables to output variables. Denoting 𝑥 the observable (and sometimes controllable) input variables and 𝑦 the output variables (response variables) and 𝜃 the values of additional unobservable input variables and tuning
parameters, which are required to obtain a calibrated computer model.
Although there is clearly a distinction between calibration variables (physical quantities) and tuning parameters (purely numeric quantities, dependent on the specific computational technique chosen), the calibration includes both types.
The model is calibrated with respect to both input variables and model parameters (numeric model refinement) but they should be dealt separately from a conceptual point of view.
A general black-box model can be expressed with the following expression:
𝑦 = 𝐺(𝜃, 𝑥)
A set of “n” experimental or observational measures are considered, denoted with “d” where:
𝑑 = 𝑑+, … … , 𝑑
-Finally, the notation of "𝜖-" is introduced for a random variable describing the difference between model prediction 𝑦 and observations 𝑑-, obtaining the following expression:
𝑑- = 𝐺(𝜃, 𝑥) + 𝜀
-At this point, the model needs to be calibrated in order to individuate the best forecasting function and related parameters.
Various statistical methods can be used for the calibration of computer simulations, and one of the most straightforward ways is performing a non-linear regression analysis with respect to the observational data and the computational model output. In this case, the problem is solved using optimization techniques to minimize the sum of the squares of the residuals between model pre- dictions and observed/experimental data. The root of the squared mean of the residuals between the experiments and the predictions is defined as:
𝑅𝑀𝑆 = 51
𝑛8 (𝑑- − 𝐺(𝜃, 𝑥)):
; -<+
A case study that applies this kind of approach is given by Fathi et al. [12].
They analysed the University of Florida campus consisting of 45 buildings. Among these buildings two of them have been chosen to be the object of the simulation.
The first building is “Rinker Hall” (figure 2.12) and it’s the school of construction management at UF. It is a three-storey building with almost 4,600 m2 gross area that was completed in March 2003. It is completely oriented on a north-south axis and two major facades of the building are facing east and west directions. However, due to implementation of large glazing facades, most parts of the
building are day lit throughout a year. Furthermore, recycled content, manufacturing proximity, low toxicity, low maintenance requirements and recyclability were considered in choosing the materials used for the building construction.
Figure 2.12 – Rinker hall model
The second building is “Hough Hall” (Figure 2.13), and it’s the graduate school of business at UF. It is a three-storey building as well with almost 6,300 m2 gross area. This facility was completed in July 2010. Unlike Rinker Hall, Hough Hall is oriented on an east-west axis with its major facades facing north and south directions and it has relatively lower window to wall ratio comparing to Rinker Hall. Similar to Rinker Hall, sustainable and energy efficient approaches were implemented for construction of the building.
Figure 2.13 – Hough Hall model
The reason that Rinker Hall and Hough Hall have been chosen to be modelled is the similarity of them as they are both colleges with relatively close gross area. Also, the HVAC systems for the two buildings are quite the same, including building automation systems, cooling equipment and towers,
heat pumps and equipment, condensate recovery, steam traps, domestic hot water heaters, variable speed control of pumps, reheat and re-cool systems, air economizers, fans and ductwork, and demand control ventilation systems.
However, the annual EUI (Energy Utilization Index) values of the buildings are not comparable and Rinker Hall is consuming much more energy than Hough Hall. The only considerable difference between the two buildings is their orientation. In Hough Hall, major facades are completely facing north and south directions while they are facing east and west directions in Rinker Hall.
For the purposes of this study, DesignBuilder v 3.2 was used to model Rinker Hall and Hough Hall. The result of the energy analysis included the annual and monthly energy consumption of the buildings in terms of room electricity, heating, cooling and lighting.
Based on the analysis, the simulated EUI values were calculated as 341 kWh/m2 for Rinker Hall and 267 kWh/m2 for Hough Hall. Furthermore, based on 3 years of utility bills data, the average actual EUI values were calculated as 355 kWh/m2 and 260 kWh/m2 for the two buildings,
respectively. We can observe that the differences in actual versus simulated EUI values are 4.71% for Rinker Hall and 9.98% for Hough Hall.
Some processes of calibration of the model have been taken in account:
1. ASHRAE Guidelines 14-2002: Measure of energy and demand savings (ASHRAE Standards Committee 2002)
2. Measurement and verification (M&V) Guidelines for Federal Energy Projects, Federal Energy Management Program (FEMP 2008)
3. International Performance Measurement and Verification Protocol (IPMVP 2002)
All of these standards use the Coefficient of Variation (CV) of the Root Mean Squared Error (RMSE) as the measure for calibration. The admissible ranges of tolerance for CV (RMSE) for monthly data calibration are ± 5%, ± 10% and ± 15% for IPMVP, FEMP and ASHRAE
respectively.
Based on these definitions, the CV values (RMSE) has been calculated for monthly data calibration and checked them in comparison with the tolerance range accepted by the IPMVP, FEMP, and ASHRAE Standard. Figures 2.14 and 2.15 show the actual versus simulated monthly energy consumption for the two buildings.
Figure 2.14 – Rinker Hall Predicted vs Actual energy consumption
Figure 2.15 – Hough Hall Predicted vs Actual Energy consumption
Furthermore, Table 2.1 shows the metered and simulated monthly energy consumption and the CV (RMSE) for the two buildings which both satisfy the tolerance range of the previously cited
Table 2.1 Metered and Simulated Rinker Hall and Hough Hall energy consumption per month
It should be mentioned that the models were developed based on assumptions that could have minor differences with either of the two buildings' actual condition. Therefore, differences have been observed between actual and simulated monthly energy consumption data for Rinker Hall. However, there was not such an issue for Hough Hall.
The difference can be caused by the occupancy patterns that may be different from the occupancy schedules assumed for the buildings throughout the year. Also, it is caused by the different plug load patterns existent in the two buildings. On-site audits can show that there are much more computers and laboratory equipment existent in Rinker Hall that cause its actual plug load density to be considerably higher than what it is in Hough Hall. This difference was calibrated by adding 1.2 kWh/sf of plug load to the monthly simulated energy consumption data for Rinker Hall. This modification is based on measuring actual Rinker Hall plug load density by using implemented plug load sensors.
This feature implies the fundamental role of occupancy in energy consumption characterization both using software or other tools.
That fact has been widely outlined in the study conducted by Menezes et al. [13], in this study it has been analysed the connection between an effective knowledge of the occupancy and the
The paper analyses the energy performance of an office building in central London that
accommodates the offices of four different companies throughout its seven floors and basement. It includes an atrium that extends to all floors (except the basement).
Each floor comprises a main open-plan office space with a treated floor area of approximately 2000 m2. The ground floor houses a large reception area and the basement houses meeting rooms and cellular offices. The building is fully air-conditioned, three rooftop air handling units (AHU) provide heating/cooling as well as fresh air to all floors and atrium. A separate system provides heating for the basement, whilst fan coil units (FCUs) provide cooling to the meeting rooms and small individual offices. Two gas-fired boilers provide hot water to all toilets and kitchens throughout the building.
The landlord is responsible for the electricity consumed by all air conditioning equipment including the AHUs, FCUs, chillers, pumps and fans as well as the Building Management System (BMS) and other control equipment. The lighting throughout the common areas of the building as well as the toilets is also supplied and maintained by the landlord. As such, the energy supplied for the landlord services is metered together, with no sub-metering for individual end-uses. Meanwhile, the
electricity supplied to the tenants for lighting, small power equipment and catering in each of the floors is metered separately. A total of 31 sub-meters provide a further breakdown for each of the 4 zones in each floor: North-East (NE), Northwest (NW), Southeast (SE) and Southwest (SW). The total energy consumption for both gas and electricity was analysed and broken down by individual end-use, a further analysis of the tenants’ consumption was undertaken. This in-depth study focused on the electricity consumption for lighting, small power and catering within each of the tenant zones, relying on monthly meter readings for each of the sub-meters as well as half hourly profiles acquired through the use of 3-phase portable data loggers connected to the
individual sub-circuits. Further data was acquired using combined plug monitor/loggers connected to individual small power office equipment such as laptops, computer screens and docking stations. These were also used to monitor the electricity consumption of catering equipment such as fridges, microwave ovens and coffee machines. Half hourly profiles for each of the pieces of equipment being monitored were reviewed in order to obtain an average daily consumption value. Where different usage modes were present (such as stand-by mode), these were recorded separately and accounted for when calculating the average daily consumption for each equipment. Occupancy patterns were also monitored by manually recording the number of occupants within the office in half-hour intervals.
Figure 2.16 – Metered Electricity consumption per floor, Metered electricity consumption per tenant, tenant occupation per floor
In figure 2.16 are shown the results in measured energy consumption for each tenant, for each floor and the floor occupation by each tenant.
Successively, the 4th floor NE zone has been chosen to be further studied and 5 predictive models of electricity consumption have been produced.
These predictions refer to the annual electricity consumption for lighting, small power and catering for this specific zone, occupied by Tenant B. An increasing level of detail was used in each
subsequent model, replacing typical assumptions of occupancy related energy consumptions with monitored data.
The parameters used for each of the electricity demands are detailed in the figure below.
Figure 2.17 – Predictions details
Results from the predictive models are illustrated in Figure 2.17. The predictions are labelled 1–5 accordingly and reflect the inputs specified in figure. As seen, the predictions are compared against the actual electricity consumption, which is not subdivided into the specific end-uses due to the limitations of the sub-metering strategy of the building.
Figure 2.18 – Predicted vs Metered Energy consumption
By using a typical compliance model in prediction model 1, the calculated electricity consumption was shown to be less than 1/3 of the actual in-use consumption. The predicted value was then increased significantly in prediction model 2 when ‘rules of thumb’ published by the Building Services Research and Information Association (BSRIA) for small power consumption were used to account for the electricity demand of office equipment.
In prediction model 3, design specifications and rules of thumb were replaced by monitoring data of installed lighting and equipment. At this point, only basic equipment was considered, and standard occupancy hours were assumed.
In prediction model 4, all installed equipment was included, resulting in an increase of
approximately 15% in the total electricity consumption. Finally, in prediction model 5, the standard occupancy hours were replaced by monitored occupancy hours.
By doing so, the predicted electricity consumption came within 3% of the actual consumption of the building in-use. This small discrepancy could be associated with the fact that the predictions were based on measurements from a single day.
In this paper is analysed the existence of a so called “performance gap” between simulated and measured consumption data, gap that can be progressively reduced by introducing more and more data regarding the case study.
What is important about the study is that introducing real occupancy schedule into the model has clearly increased the model accuracy and for this reason it is necessary to dispose of items capable of provide such useful data.
Once the consumption data, factual or forecasted, is available, to assess the energy efficiency of a building is necessary to consider certain parameters.
Because the university building sector is a very peculiar one, various influential parameters need to be considered, for example:
• Building typology • End-use
• Deviation from the climatic reference conditions • Operational management
Building typology: University stocks are very different from one another, due to their location,
climate, year of construction and many other aspects. 3 main buildings typologies can be identified:
• Old Buildings • Recovered Buildings • New Buildings
Each of the typologies previously mentioned has different characteristics that influence energy efficiency.
Usually universities located within an inhabited centre are composed by Historical and Recovered Buildings, whether when built in a dedicated area, such as campuses, are mainly composed by New Buildings.
An example of such assumption is represented by University of Pavia [14] which has 77,000 m2 surface occupied by historic and historical buildings, located in the town centre, while 60,000 m2 were built before the II World War, and 85,000 m2 in the ‘80s, in other areas occupied by the modern edification, for a total built area of 230,000 m2. (Figure 2.19)
Figure 2.19 - Location of the University buildings in the town centre (Historical Buildings, HB, yellow, right side), in the modern expansion (Contemporary Buildings, CB, green) and outside the town (CB, blue, left side)
Old buildings are made by thick walls of bricks which are characterized by great thermic inertia given the little reliability given to the heating systems at the time.
An example of this building typology is shown in Figure 2.20. The thermal delay resulting from inertia meant that when the temperature outside was high, the building accumulated thermal energy that was released while the outer temperature was lower so that less energy was required by the heating system. Layer Thickness (cm) Plaster 1,5 Full Bricks 37,5 Plaster 1,5 Plaster Full bricks Plaster
During the following years heating systems efficiency and reliance increased considerably and consequently the building technique evolved.
At first the method adopted consisted in increasing the walls’ thermic resistance while preserving some inertia, utilizing concrete and air cavities (figure 2.21).
Successively, buildings have begun to be built with lighter materials meaning less construction time, little thermal resistance and inertia.
An example of such building’s wall is shown in Figure 2.22.
These buildings’ thermal behaviour is characterized by fast thermal waves penetration so that the maintenance of the comfort conditions is mainly due to the air-conditioning system.
Layer Thickness (cm) Plaster 1,5 Full bricks 12,5 Cave Chamber 5 Full bricks 12,5 Plaster 1,5 Full bricks
Plaster Cave chamber
Figure 2.21 – Composition of a wall with air cavity
Newest buildings are characterized by large windows, equipped with shading systems that are able to reflect the light coming from the sun during hot periods, and allow its access during colder periods. It’s true, however, that these windows should be double glazed and possess an adequate thermal resistance so that an excessive thermal loss does not occur.
Walls are a little heavier than the previous typology in order to provide the building with higher thermal resistance (figure 2.23)
Layer Thickness (cm) Plaster 1,5 Linoleum 0,2 Fiber- Reinforced Cement 1,5 Wood Fiber 2 Drywall 1 Plaster 1,5 Linoleum Fiber-Reinforced Cement Drywall Wood Fiber
Another aspect that characterize a building and that could help in retrace the building typology is the external surface area to heated volume ratio (SVR).
Old buildings are characterized by very high ceilings and a low SVR, whether newer buildings to reduce thermal waste have lower ceilings and consequently a higher SVR.
An example of such assumption is shown in the following figure, where are represented two lecture rooms of the University if Pisa. Room A21 is located into an old building (Polo A), Room SI5 is located within a relatively new building (Polo B) and rooms F08 and F02 are located in a recovered building (Polo F) (Table 2.2).
Table 2.2 – Classroom’s SVR comparison
Room A21 SI5 F08 F02
SVR (m2/m3) 0,2 0,345 0,177 0,3
Even if recovered buildings are difficult to standardize, the difference between old and new building is plain. Layer Thickness (cm) Plaster 1,5 Insulating 10 Half-bricks 25 Plaster 1,5
Figure 2.23 – New buildings walls composition
Plaster
Plaster Insulating Semi-bricks
In an early stage of the benchmarking procedure, while assessing new buildings to be built, Magrini et al. [14] propose the following index (Figure 2.24):
Figure 2.24 – Magrini’s Index
The analysis will focus on the parameters related to the building envelope and shape.
The first aspect is considered during the average annual fuel consumption estimation, because it derives from a computer simulation that requires a thorough description of the building envelope and the energy system (physical based).
The shape assumed is considered through the BSF factor, function of the SVR.
It is important to point out that whether the SVR is not inferiorly limited, it is so superiorly given the minimum room height defined by the regulations.
The BSF is higher for lower SVR meaning that a room with a higher SVR is characterized by a lower thermal dispersion, thereafter a lower energy demand and a lower IEN, analogous of the EPI (energy performance index).
End-use: As briefly explained in the previous chapter, university buildings are characterized by
multiple end-use within the same envelope.
A clear example of such assumption is given by the comparison between the daily power curves and energy parameters of two buildings within which different activity have been performed (Figure 2.25 and 2.26) (Table 2.3).
These are the science building (Figure 2.25) and the law building (Figure 2.26) of the University of Siena energy consumption, as reported by Ruzzenenti et all. [15].
Figure 2.25 – Daily Power curves of University of Siena’s Science Building
Tab.2.3 – Comparison between energy parameters of Law and Science Building in Siena University
Energy Intensity year 2003/2004 University of Siena
Science Building Surface - m2 (students) 35053 (2354) Electricity – kWh/m2 146.95 Heating - kWh/m2 140.90 Total - kWh/student 4343.60 Law Building Surface - m2 (students) 18336 (5468) Electricity – kWh/m2 76.33 Heating - kWh/m2 70.74 Total - kWh/student 493.66
Despite the fact that the Science Building covers nearly twice the surface occupied by the Law Building, its power peak is almost 3,7 times higher and its specific total energy consumption is almost two times higher.
That doesn’t necessarily mean that Law Building is more energy efficient because the two buildings has different activities carried out within them.
Into the science building there are, aside from lecture room, cafeteria, toilets and common spaces that present also in the building of law, also laboratories, computer labs, servers and a laboratory containing 70 m2 of Guinea Pig, where AC and lights run relentlessly.
That means an inevitable increase in the energy consumption, but the term energy efficiency doesn’t merely indicate the amount of energy consumption but its proper use.
For that reason, it may happen that, even if the Science Building consumes nearly twice compared to the Law Building, the Science building cloud result more energy efficient, that because energy efficiency depends on the way the energy is used, not its amount.
A method to consider such aspect is proposed by Altan et all. [16] describing some buildings in terms of its end-use relative composition (figures 2.27a, 2.27b, 2.27c, 2.27d, 2.27e).
Figure 2.27a – Pie chart of the composition of building CSB1 Figure 2.27b – Pie chart of the composition of building CSB2
Figure 2.27c – Pie chart of the composition of building CSB3 Figure 27d – Pie chart of the composition of building CSB4
Figure 2.27e – Pie chart of the composition of building CSB5
Figure 2.27g – CSB2 (a) Gas Consumption and (b)Electricity consumption vs benchmarks.
Figure 2.27h – CSB3 (a) Gas Consumption and (b)Electricity consumption vs benchmarks.
Figure 2.27i – CSB4 (a) Gas Consumption and (b)Electricity consumption vs benchmarks.
Figure 2.27l – CSB5 (a) Gas Consumption and (b)Electricity consumption vs benchmarks
In the figures there are 2 descriptions, (a) is more accurate and comprehend more categories, (b) is a simplified classification, which is more useful for this analysis.
For each end-use class, reference values have been chosen for both electrical and gas consumption, shown in Figure 2.28.
Figure 2.28 – Benchmarks reference values
Subsequently, for each building end-use compositions specific benchmarks have been calculated. The “Good” and “Typical” benchmarks have been calculated according the specific end-use composition, doing a weighted average of the reference values listed above.
The result of such procedure is shown in figures 2.27f, 2.27g, 2.27h, 2.27i, 2.27l. It’s now possible to have an idea of the various buildings’ energy efficiency.
Figure 2.29 –Gas and Electricity Benchmark references and actual values compared for each building
Observing Figure 2.29, where the comparison between each building’s reference benchmarks and actual one is shown, is possible to notice how building CSB4 gas consumption is less than the “Good” benchmark would expect, whereas the other buildings have scarce gas consumption efficiency, consuming more than the “Typical” benchmark would expect.
Meanwhile considering electricity consumptions building CSB4 consumes less electricity than the “Good” benchmark would expect, whereas building CSB1, CSB2, CSB3, CSB5 has an electricity consumption between the “Good” and “Typical” values.
This analysis suggests improving the other buildings’ efficiency, considering possible operational changes or refurbishment taking as an example the way CSB4 is managed.
In order to apply this method, it is necessary to use appropriate reference values, generally arising from a buildings’ database. For that reason, the more data are available in said database, the more suitable the reference values will be.
Deviation from the climatic reference conditions: This is an exclusively operative aspect because
it is possible to account for the building energy behaviour under certain climatic condition only once it has already been built and it’s operating.
Generally, University buildings’ energy systems are managed by external companies which sets inner temperature set point, air exchange rate and all the other parameters according to the legislation taking certain standard conditions as a reference.
When said standard conditions don’t occur, it may happen that automatic regulation techniques are not enough and for this reason the comfort conditions necessary for the smooth running of the activities inside the building may not be verified.
Therefore, manual regulation is necessary as a consequence of which energy wastes occur, affecting the energy efficiency of the building.
The tool generally used to determine reference conditions is the Heating Degree Day (HDD), defined as follows:
Heating degree days are defined with respect to a base temperature, the outside temperature above which a building needs no heating. Base temperatures may be defined for a particular building as a function of the temperature that the building is heated to, or it may be defined for a country or region for example. In the latter case, building standards or conventions may exist for the temperature threshold (figure 2.30):
Figure 2.30 – Different nations base temperature
HDD are often calculated using simple approximation methods that use daily temperature readings instead of more detailed temperature records such as half-hourly readings. One popular
approximation method is to take the average temperature on any given day and subtract it from the base temperature.
𝐻𝐷𝐷 = 𝑇@ABC ECFGCHAIJHC − 𝑇KA-LM NOCHAPC
If the value is less than or equal to zero, that day has zero HDD. But if the value is positive, that number represents the number of HDD on that day. This method works satisfactorily if the outside air temperature does not exceed the base temperature. In climates where this is likely to occur from time to time, there are refinements to the simple calculation which allow some 'credit' for the period of the day when the air is warm enough for heating to be unnecessary.
Each region has its reference HDD, generally referred to a heating season duration, for example in Italy it is possible to find such values at the ENEA website ([17]).
The effectiveness of this parameter needs to be assessed.
HDD are a simplified way to represent the rigidity of the heating season in terms of outer
temperature, but it doesn’t consider other atmospheric agents’ effect like wind, sun radiation and/or rain.
The general assumption that is made is that those agents’ effect is balanced and it’s globally zero. Nevertheless, researches have been conducted in order to verify that HDD are an effective
parameter.
For example, the previously cited study of Altan et all. [16] compares Gas consumptions and measured HDD (figure 2.31).
Figure 2.31 – CSB1 CSB2 CSB4 CSB4 Gas consumption plotted against the number of positive heating degree days (using 15.5°C)
It is evident how Gas consumptions follows HDD trend, for this reason iT would indicate a good indicator.
Corgnati et all. [18] propose a procedure of energy consumption assessment that consider the influence of HDD, which provides some useful insights to this research.
The sample of buildings under analysis is made up of 138 buildings (117 high schools, 9 office buildings and 12 homes for school keepers) of the Provincia di Torino, located some in Torino town and some in other towns of the province of Torino.
At first, they aimed at gathering useful information to build a database. For that purpose, a form was set up in order to collect data regarding buildings’ location and geometrical data as gross heated volume or useful floor surface, actual degree-days, type of fuel, metered annual primary energy consumption for space heating, metered energy consumption for space heating, duration of the heating period.
Disposing of such data, they assume a linear dependence of the energy consumption both on the degree-days and of the duration of the heating period.
In support of this hypothesis, the linear correlation between average monthly specific billed fuel consumption and monthly degree-days was verified.
Figure 2.32 shows such relation for the whole sample of the gas heated buildings.
The slight dispersion of the dots around the line shows the influence of stochastic factors linked to the user’s behaviour, such as internal gains, ventilation, temperature set points, etc.
Anyway, a good linear correlation is shown (R2=0.88).
As a consequence, in the analysed building stock the use of degree-days in the normalization of the energy consumption appears as a suitable way to neutralize the effect of the climate.
Successively, they elaborate an index to predict energy consumption, related to the actual climate data.
Figure 2.32 – Monthly specific fuel consumption vs. monthly degree-days gas heated buildings in Torino.
This index is defined as the ratio of the energy supplied by the heating system (obtained from the metered supplied thermal energy) to the gross heated volume (V), referred to the conventional degree-days of the site (DDc) and to the conventional heating period (dc).
𝑄𝑃B,S = 𝑄𝑃 𝑉 × 𝐷𝐷S 𝐷𝐷 × 𝑑S 𝑑
Where DD and d are, respectively, the actual number of degree-days of the site and the actual duration of the heating period and QP is the seasonal metered heat supply.
As previously mentioned the conventional degree-days are established at national level for different sites in order to characterize the local winter climate, while the conventional heating period is defined according to the destination of use of the buildings.
The effectiveness of such index is shown in figure 2.33 and figure 2.34, a good agreement is shown between annual measured heat supply and corrected conventional heat supply.
On the contrary, the monthly heat supply data point out a strong discrepancy between measured and calculated energy use. This result can be explained by the uncertainty of monitoring periods, i.e. the meters can be read non-exactly at the end of each month.
Figure 2.33 – Measured specific heat supply vs. corrected conventional specific heat supply for the school buildings
Figure 2.34 – Measured heat supply vs. corrected conventional heat supply for the whole building stock
Moreover, the confidence interval of operational rating becomes larger for short assessment periods (month), especially in those months for which the assumption of linear correlation between energy use and outdoor temperature is less acceptable because of a large amount of free heat gains. With reference to school buildings, which represent more than 95% of the global energy
consumption, the deviation between the measured and the assessed energy use is about 7%. This deviation becomes about 6% for the entire building stock, even if more significant relative deviations are shown for offices and residential buildings.
The analysis above works well when assessing a whole building stock, while when the analysis focuses on a single building there are other consideration to be made.
In this case, the linear dependence between energy consumption and HDD is not verified so it’s not possible to use the energy index proposed by Corgnati et al. but a new relation must be found.
Following Corgnati’s insight, the shape of the normalized energy consumption index which can be used to compare buildings located in different climate area is:
𝐸𝐶𝐼; = 𝐸𝐶
𝑉 ∙ (𝑛𝐷𝐷F) ∙ (𝑟𝑑B)
Where 𝐸𝐶𝐼; is the specific energy consumption normalized index, 𝐸𝐶 is the energy consumption, V is the heated volume (Heated surface could be used as well), DD are the actual degree days, d is the actual heating period, while n, m, r, s are coefficients which shows how the correlation between EC and DD and d is not linear.
Moreover, given the aim of this analysis it is important to make further considerations regarding HDD definition.
In University buildings, during heating season (same assumptions could be made during cooling period as well), sometimes maximum outer temperature exceeds the base temperature previously used while defining HDD.
When that happens the building’s envelope could overheat therefore necessitating less heat in order to obtain optimal comfort conditions.
For this reason, is necessary to use a more accurate method like the one showed in the following figure (figure 2.35a, 2.35b) [23], which can be used both while heating and cooling season. Heating season:
Cooling season:
Figure 2.35b –Accurate Degree Days calculation method for cooling season
Operational Management: As previously mentioned university buildings are characterized by
occupancy schedule that vary widely during the year, given the fact that lessons and exams period follows one another.
During high occupation periods lecture rooms are characterized by elevated CO2 concentration, elevated internal heat gain and an elevated number of plugged devices.
Elevated CO2 concentration means that the air should be renewed more often to maintain a healthy environment, elevated internal gain means that fewer energy is needed from the air conditioning system to maintain the temperature set point during colder periods, and higher energy is needed during warmer periods.
The knowledge regarding the actual occupancy is a crucial factor in reducing energy consumptions during the operational phase, the approaches generally used are the statistical forecast using certain occupancy model and the use of sensors that are able to measure specific parameters related with occupancy.
In this aspect of the benchmarking procedure arises the importance of considering the influence of people in the building, for that reason a more in-depth analysis will be given.
Once all the data have been gathered is possible to set-up a proper benchmarking procedure. The first step consists in calculating each building’s benchmark considering all the parameters previously mentioned. It is obvious that for the benchmarking procedure to be effective it needs a large amount of buildings.
Once all the benchmarks have been calculated is possible to build a cumulate of the benchmarks value, that will have the shape of the curve reported in Figure 2.36:
Figure 2.36 – Benchmarking procedure’s reference values
Where on the x-axis is reported the benchmark value, and on the y-axis the frequency with which it occurs.
The following step is to set the reference values, subdividing the cumulate in intervals or levels. The levels size can be set at will, the more levels the more the benchmark will be accurate. For example, in the benchmark reported have been chosen to set 4 levels:
• Below the lower quartile from 0% to 25% • The lower quartile from 25% to 50% • The median quartile from 50% to 75% • The upper quartile from 75% to 100%
At this point of the benchmarking procedure it is possible to assess each building energy efficiency compared to the rest of the building stock. It will be enough to calculate a building’s benchmark, observe to which quartile it belongs and know how well he behaves in terms of energy efficiency.
3.
CO
2as a mean to analyse the buildings’ occupancy
In the previous chapter has been highlighted the importance of the knowledge and the forecasting of occupancy pattern in energy efficiency benchmarking and consumption assessments.
There are many occupancies detecting models currently being implemented such as RFID, Video cameras, electromagnetic field detection etc.
Each of the mentioned model is characterized by some defects that affect their effectiveness and/or application.
A method that presents no big drawbacks and that is cheap, easy to implement and gives no privacy issues is the CO2 detection.
A research that summarises the influence of occupancy in the thermal balance and energy related consumption is presented by Tagliabue et al. [19]:
The approach proposed is summed in the figure shown below (Figure 3.1):
Figure 3.1 – influence of occupancy in the thermal balance
The people in a building zone (e.g. classroom, laboratory) influence the thermal balance and ventilation rate because of:
• CO2 production:
The air change rate is determined based on the amount of people that are present in the building zone. In order to be able to guarantee CO2 concentration levels lower that a certain
maximum acceptable level a congruent amount of fresh air for each person has to be provided.
The air change rate is determined according to the following formulas:
𝑛 = 𝑣F-;𝑉∙ 𝑖B∙ 𝐴 𝑉A,] = 𝑉 ∙ 𝑛
n is the specific number of air changes [h−1];
vmin is the specific external air flow required in the occupancy period [m3/h per person]; Is is the density of occupants [person/m2];
A is the surface area of the zone [m2]; Va,k is the air flow rate required [m3/h]; V is the net volume of the thermal zone [m3]
The variation of CO2 concentration level over time within a certain building zone can be calculated according to the following formula:
𝑐 = _ 𝑞 𝑛𝑉a ∙ b1 − c 1 𝑒;Ief + (𝑐g − 𝑐-) ∙ c 1 𝑒;Ie + 𝑐
-where c is the CO2 concentration [m3/m3]; “q” is the CO2 produced by occupants [m3/h]; “V” is the net volume of the thermal zone [m3];
“n” is the specific number of air changes for category use [h−1]; “t” is the time [h];
The occupants generate a quantity of CO2 that can be determined according to the formula:
𝑞 = 𝑞G∙ 𝑛h
“q” is the CO2 produced by each occupant in the zone (0.05m3/h/person) [m3/h]; “no” is the number of occupants [–].
