Model calibration for building energy efficiency simulation
Giorgio Mustafaraj
⇑, Dashamir Marini, Andrea Costa, Marcus Keane
Informatics Research Unit for Sustainable Engineering, National University of Ireland, Galway, Ireland
h i g h l i g h t s
Developing a 3D model relating to building architecture, occupancy & HVAC operation.
Two calibration stages developed, final model providing accurate results.
Using an onsite weather station for generating the weather data file in EnergyPlus.
Predicting thermal behaviour of underfloor heating, heat pump & natural ventilation.
Monthly energy saving opportunities related to heat pump of 20–27% was identified.
a r t i c l e i n f o
Article history:
Received 21 July 2013
Received in revised form 29 April 2014 Accepted 14 May 2014
Keywords:
Building energy simulation Energy efficiency Natural ventilation Underfloor heating Heat pump water to water Model calibration
a b s t r a c t
This research work deals with an Environmental Research Institute (ERI) building where an underfloor heating system and natural ventilation are the main systems used to maintain comfort condition throughout 80% of the building areas. Firstly, this work involved developing a 3D model relating to build- ing architecture, occupancy & HVAC operation. Secondly, the calibration methodology, which consists of two levels, was then applied in order to insure accuracy and reduce the likelihood of errors. To further improve the accuracy of calibration a historical weather data file related to year 2011, was created from the on-site local weather station of ERI building. After applying the second level of calibration process, the values of Mean bias Error (MBE) and Cumulative Variation of Root Mean Squared Error (CV(RMSE)) on hourly based analysis for heat pump electricity consumption varied within the following ranges:
(MBE)hourlyfrom5.6% to 7.5% and CV(RMSE)hourlyfrom 7.3% to 25.1%. Finally, the building was simulated with EnergyPlus to identify further possibilities of energy savings supplied by a water to water heat pump to underfloor heating system. It found that electricity consumption savings from the heat pump can vary between 20% and 27% on monthly bases.
Ó2014 Elsevier Ltd. All rights reserved.
1. Introduction
Buildings contribute to 40% of western countries energy con- sumption[1]and it is important to reduce their energy consump- tion and their related carbon foot-print. In order to slow the energy demand growth and reduce the amount of energy used associated with buildings, it is first important to understand how energy is distributed throughout a building, and how building parameters contribute to energy consumption and demand [2]. Energy con- sumption analysis of buildings is a difficult task because it requires considering detailed interactions among the building, HVAC sys- tem, and surroundings (weather) as well as obtaining mathemati- cal/physical models that are effective in characterizing each of those items. The dynamic behaviour of the weather conditions
and building operation, and the presence of multiple variables, requires the use of computer aid in the design and operation of high energy performance buildings. Drawbacks in using computer simulations include the considerable amount of detailed input data and time from even experienced users[3]. Furthermore, simulation tools may not be cost-effective at the first stage of analysis, which makes others tools, such as screening tools, a better’s option. Some of them are based on statistics and other on simulations[4]. On- line building energy predictions based on neural networks and genetic algorithms can also be used in some applications [5].
Knowledge of energy demand profile and energy consumption of buildings facilitates the implementation of actions to reduce build- ing operation costs[6]. For instance, hourly energy consumption data is required for the application of different screening technol- ogies. This information is used to assess the use of energy-efficient technologies in buildings by comparing conventional heating and cooling equipment with technologies such as cogeneration, http://dx.doi.org/10.1016/j.apenergy.2014.05.019
0306-2619/Ó2014 Elsevier Ltd. All rights reserved.
⇑Corresponding author. Tel.: +39 3771760158.
E-mail address:[email protected](G. Mustafaraj).
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Applied Energy
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trigeneration, ground-coupled heat exchangers, and solar thermal and solar photovoltaic technologies, among others. EnergyPlus, this is a new generation building energy analysis tool, bears many advantages when compared to its ancestors and is suited to ana- lyse building performances with non-normal building systems especially for large office buildings [7,8]. In this research work, the complexity of the building and its system is a major factor in choosing EnergyPlus to conduct the simulation among various other energy simulation tools (such as TRNSYS, eQuest, IES and ESP-r). EnergyPlus is capable of simulating the combination of multiple systems for one building and it allows the analyser to define the components in great detail, so as that most of the aspects of the design can be represented by the models. Moreover, it is very flexible and easy to switch between different seasonal states of the systems using schedules definition in EnergyPlus [7,8]. Griffith et al.[9]employed EnergyPlus to study the influence of some advanced building technologies over the building perfor- mance of a building in Teterboro airport and DOE-2.1E to analyse the effect of such common measures as optimized envelope system and schedules.
Ellis and Torcellini[10]carried out research on the reliability of EnergyPlus in simulating tall buildings and the outcomes from their research proved accuracy and reliability of EnergyPlus in sim- ulating on a tall building.
Xiaoqi et al.[11]proposed an innovative Energy Saving Align- ment Strategy (ESAS) to reduce building energy demands. They explored the application of ESAS in the context of public housing.
Simulation results with EnergyPlus reveal 2.1–42.0% in energy sav- ings compared to random apartment assignments depending on geographic location, with the highest energy reductions occurring in cities with mild climates, where the range of occupant thermo- stat preferences coincides with the natural indoor temperature range. Mohamad et al.[12]calculated the annual energy load of the windows offset thermal bridges for a typical French house.
Co-simulation was used to incorporate the two dimensional heat transfer effects (by developing a MATLAB code) to EnergyPlus, a whole building energy simulation program. Results analysis showed that the windows offset thermal bridges energy load per- centage of the total house load constitutes around 2–8% depending whether exterior walls have interior insulation or not. In addition, they found that applying a coating reduces the windows offset thermal bridge load by about 24–50%. Jain et al. [13] a sensor- based forecasting model using Support Vector Regression (SVR), a commonly used machine learning technique, and apply it to an empirical data-set from a multi-family residential building. They examined the impact of temporal (i.e., daily, hourly, 10 min inter- vals) and spatial (i.e., whole building, by floor, by unit) granularity have on the predictive power of our single-step model. Their results analysis indicate that sensor based forecasting models can be extended to multi-family residential buildings and that the opti- mal monitoring granularity occurs at the by floor level in hourly intervals.
Compared to past research works[14–17]the calibration pro- cesses developed by the present researchers had the following advantages: (1) weather data file used in this research was built based on the data collected from onsite weather station. Most of past researchers used historical data file coming from weather sta- tion positioned outside the city where the building is positioned (for example airports, where the distance can be considerably high). Thereby, the weather condition can be slightly different and the accuracy of the calibration can be affected considerably;
(2) this research is a complete analysis that includes a calibration process and then the necessary actions to increase energy savings.
Past research work on calibration dealt mainly with calibration process without considering any further opportunities of energy savings; (3) HVAC plants involved in this building are very complex
which includes mainly underfloor heating, forced ventilation and natural ventilation, while past research works dealt mainly with forced ventilation supplied by AHUs; (4) the calibration process in this research was based on the most advanced calibration meth- odologies developed by Yoon and Lee[17]and Bertagnolio [18], where in addition the HVAC plants involved were more complex.
The objective of this study is to present a methodology that starts with calibration and then continues with the necessary actions for improving hourly building energy consumption. This article firstly describes the ERI building, HVAC plants and natural ventilation. Secondly, the two levels of the calibration methodol- ogy are presented and then applied to ERI building. Thirdly, the building is simulated to identify further opportunities of energy savings. Finally, a conclusion with future research work is provided.
2. Building description 2.1. General information
The ERI building is a three-storey 4500 m2research building containing offices, computer laboratories, wet laboratories, a clean room and controlled temperature rooms.Fig. 2-1 is its 3D view generated with DesignBuilder[19]according to design documents.
The building is used as ‘‘Living Laboratory’’ by the Informatics Research Unit in Sustainable Engineering (IRUSE) of the University College Cork (Cork, Ireland) and the Irish strategic research cluster ITOBO[20]to serve as a full-scale test bed for Intelligent Buildings demonstrating building performance concepts[21]. The geometry of the building model was derived from Mechanical Ventilation drawings, originated. DXF files were created from the AutoCAD drawings (DWG). A number of techniques were employed by the author to ensure an accurate representation of the final construc- tion. Information was gathered through: (a) basis of design reports by the Architects as built drawings, and (b) extensive building sur- veys conducted by the author, in relation to geometry, physical constructions, equipment utilization surveys and review of Build- ing Management System (BMS) logs. The DXF files were imported into DesignBuilder and the building model was created by ‘‘trac- ing’’ over the imported back ground. The computer model repre- sents the building as a series of individual zones (see Fig. 2-2).
These zones reflect the different thermal characteristics of the building in terms of both the physical properties and the properties of the air. The zones correspond largely to the heating and cooling zones dictated by the original design. The building is reinforced
Fig. 2-1.The ERI building 3-D view of design model.
G. Mustafaraj et al. / Applied Energy 130 (2014) 72–85
concrete structure providing high levels of thermal mass to allow for natural and mechanical ventilation with night cooling as required. The entire building is a single fire zone and air circulation is encouraged thought the building by the use of openings between laboratories, loosely fitting sliding doors, and doors held open to stair stacks. The two internal stair stacks double up as ventilation stacks, drawing air though the building during warm weather and as part of the automated night cooling strategy. The dual func- tion of the stairs allows the benefits of building atrium without the additional cost associated with a dedicated atrium stack[21].
2.2. HVAC system
Fig. 2-3illustrate schematics overview of HVAC systems. The building is heated by an underfloor heating system that is primary supplied by a geothermal heat pump that taps into a water supply fed from a culvert running adjacent to a nearby river. The water is preheated by heat recovered from the cold stores and heat gener- ated by the solar thermal array (seeFig. 2-3). As a heat pump’s per- formance is closely related to its flow temperature, this strategy has the potential to provide considerable improvements in heat pump efficiency. The controls system controls the heat pump com- pressor in order to provide the required heating system flow tem- perature. The required flow temperature is based on a weather compensation routine during the day and a fixed (user adjustable) set point during the night. The purpose of this dual system is to maximise the duty of the heat pump when operating under night rate electricity (11 pm–11 am). If less than three rooms require heat then the heat pump shall not go through the start-up sequence and shall remain off (or turn off if already on, after going through the shutting down sequence).
The underfloor heating operates at a maximum temperature of 38°C. Overall, the heat pump (COP = 2.4–4.2) provides 80% of the building’s heating requirements, with the balance provided by a condensing gas boiler sized to act as a complete back-up system.
The heat pump water output, with a temperature of between 32°C and 35°C, can be heated by a further 2–3°C, by exchanging heat with the hot water that is coming out from the boiler through
the HE-4 (seeFig. 2-3). Underfloor heating is served by 12 mani- folds located in various locations throughout the building. Each manifold contains individual zone control valves for each circuit.
Each zone is controlled by a room thermostat. The individual room zoning has an installed design capacity of 90 W/m2[21,22]. There are only a few zones inside the building that are not served by the underfloor heating system. These zones are mainly positioned on the lower ground floor (LG02, LG13, LG16, LG19 and LG21, see Fig. 2-2).
Five air handling units (AHUs) are provided to serve internal areas of the building such as the clean room, toilets and internal stores (seeTable 3-5). An underfloor heating system is also pro- vided to the areas supplied by AHUs. All handling units are fitted with thermal wheels to recover waste heat. The thermal wheels have removed the need for heating coils within the air handling units. A Cooled Beam system positioned at Lower Ground floor is used to provide cooling to some zones (LG12, LG23 and LG24, seeFig. 2-2). A central, single-duct forced-air is provided by an AHU5 system which supplies conditioned ventilation air to these zones.
Sensible cooling is achieved by circulating chilled water through ceiling mounted cooled beam units. The chilled water temperatures range from 10 to 15°C and are obtained by releasing heat to the water coming from the aquifer through the heat exchanger HE-01 (seeFig. 2-3). Chilled water flow rate through the cooled beam units is varied to meet the zone sensible cooling load. The design properties of the Cooled Beam system are given inTable 3-7. No underfloor heating is provided to these zones.
The solar thermal collector composed of 28 flat collectors is installed to provide hot water with the remaining domestic water load provided by a direct gas fired water heater (seeFig. 2-3). In the present research work an accurate analysis was conducted to define the heat produced by the solar panels throughout the year 2011. It was verified that this heat is only 3–5% of the heat required by the building. Consequently, solar panels were included into EnergyPlus model developed but during calibration process no par- ticular attention will be given to them. Fume hoods are also installed in conjunction with the motorised air intakes for each Fig. 2-2.The ERI building schematic representation of each floor[21,22].
room in which they are present. In practise the fume hoods extract air from the spaces when switched on by an occupant. Make up air is drawn through specially motorised air intakes in the façade walls at high level. The intake dampers open once a fume hood has been operated and remain open unless the fume hood is deac- tivated. The extraction fans have been installed with an outside air by pass T-damper to allow the volume of air exhausted from the space to be reduced, without reducing fan speed.
2.3. Natural ventilation
Apart a small areas of the building that occupy the central core of the building (such as WCs, cold rooms, clean rooms and stores) that are mechanically ventilated, the majority of the building is naturally ventilated using a single sided strategy with localised extract fans to provide spot cooling. As depicted inFig. 2-4, a fea- ture of the building is the use of natural ventilation as make up air for laboratories containing a number of fume hoods. Breakout areas forming atria in the centre of the building also allow the pos- sibility of stack assisted and cross flow ventilation dependant on the occupants opening doors to the corridors, which feed the atrium[21]. The natural ventilation strategy is complemented by the use of exposed mass through the building. A neat exposed ser- vices installation is achieved by building slab beams into the build- ing structure, allowing services to travel smoothly between rooms under a continuous flat slab (see Fig. 2-5). Slab beams were designed to increase the area of exposed concrete which enhances convective heat transfer and improve its thermal mass exchange.
They are oriented parallel to the prevailing wind direction and help conduct cross-ventilation across the building’s entire width. The ventilation strategy includes a number of apertures used to encourage quality ventilation and limit summertime overheating well within acceptable limits.
Laboratories are located on the majority of the lower ground floor and the north side of the ground and first floors. Office and
computer laboratories are located on the southern side of the building on the ground and first floors to take advantage of passive solar heating and natural day lighting. These areas are predomi- nantly naturally ventilated using a single sided strategy with local- ised extract fans to provide spot cooling.
Motorized windows are provided to labs and offices to facilitate the operation of the automated day cooling strategy and to provide a high level of ventilation to remove warm air from the highest part of the office spaces. These windows are provided with local user controls but are over-ridden and closed by the BMS at the end of each day. Specifically, motorized windows are schedule to open and close when the indoor air temperature reaches 22°C and 20°C respectively. An automated night cooling strategy is pro- vided in conjunction with the motorized windows at the top of the stairs. The night cooling strategy will operate within the time sche- dule if the indoor temperature in a room goes above 25°C during the day. In addition, during the night cooling time schedule, if the wind speed is less than 5 m/s and outside temperature is above 16°C then the windows shall open to lower the room temperature to 19°C. Night cooling strategy operates from May to September when underfloor heating system is turned OFF. In this work to sim- ulate natural ventilation, EnergyPlus used AIRNET/CONTAM model [23,24].
2.4. Lighting system and daylighting
The lighting luminaires are typically T5 type fluorescent with high frequency control gear and hence has a 50% radiant/convec- tive split. The system operates based occupant sensor control and daylight compensation. Task lighting is used in the laboratories.
Limiting lighting loads is important both for reducing lighting energy requirements and reducing heat gain from lighting systems.
High frequency fittings have been used throughout the building, including task lights and fume cupboard lighting. As a high lighting level is required on lab benches, task lights have been used Fig. 2-3.Schematic overview of HVAC plants[21,22].
G. Mustafaraj et al. / Applied Energy 130 (2014) 72–85
produce considerably reduced lighting loads. All other internal lighting (with the exception of the lab task lights) has been fitted with occupancy and daylight dimming controls. The lab task lights are left on user control as the users will be handling chemicals and it is important that the lights do not automatically turn of at an inappropriate time. LED fittings are used for external lighting in order to minimize energy consumption and provide a low mainte- nance lighting solution[21]. Lighting is modelled in this research work utilizing the Radiance tool used by EnergyPlus[24].
3. Model calibration and further opportunities of energy savings
Building energy models were developed using EnergyPlus Ver- sion 8.0[24]. Since EnergyPlus is a ‘stand-alone’ simulation pro- gram that lacks a user-friendly graphical interface, a number of tools were used to build each model and to ensure that the models accurately represented systems and energy use. To begin with, DesignBuilder[19]was used to generate executable IDFs based on a select number of modelling inputs where all of the ASHRAE 90.1 design standard baseline system types were included[25]. Design- Builder was chosen because of its ability to generate geometry, structure and HVAC system, without the hassle of manually adding them into IDF or using the Google SketchUp plugin for EnergyPlus, Open Studio[19]. In addition, the underfloor heating system and heat pump are added into IDF file manually because DesignBuilder does not provide for them yet. Temperature, relative humidity,
wind velocity and direction, solar radiation and other weather data variables data for year 2011 were collected from the local weather station positioned at a height of 12 m based at the roof of ERI build- ing. EnergyPlus weather converter was used to build the weather data file in CSV format to be used then in EnergyPlus for energy sim- ulation. Model validation will be continued in the calibration pro- cess that will be discussed in the next section.
3.1. Previous research works on calibration
Simulation is commonly held to be the best practice approach to performance analysis in the building industry[26]. However, there are significant discrepancies between simulation results and the actual measured consumption of real buildings.
Thereby, calibrating these models to measured data can be used to identify and estimate savings as well as support investments grade Energy Conservation Measures by analysing retrofit options in detail[14]. The calibration of a forward building energy simula- tion program, involving numerous input parameters, to common building energy data is a highly under-determined problem that would result in a non-unique solution[27]. As noticed by Kaplan et al.[28], it will never be possible to identify the exact solution to the calibration problem and sensitivity issues may be of primary importance in the calibration field. A second constrain relies on the fact that calibration requires a dynamic matching over one year between computed and measured values and not a static one at one condition[16]. These elements make the calibration of build- ing energy simulation models challenging. Reddy and Maor[16]
explored a variety of tools, techniques, approaches and procedures commonly used to calibrate simulated building energy models to measure data, in an effort to develop a more systematic approach to model calibration.
The most common calibration methods can be classified as pro- posed by Reddy[29]:
Manual iterative calibration based on the user’s experience and consisting in an adjustment of inputs and parameters on a trial-and-error basis until the program output matches the known data.
Graphical and statistical methods– calibration based on spe- cific graphical representations and comparative displays of the results to orient the calibration process.
Automated calibrated methods– calibration based on special tests and analytical procedures involving specific intrusive tests and measurements.
Fig. 2-4.Natural ventilation and passive heating ERI building[21,22].
Fig. 2-5.Natural ventilation strategy within slab beams[21,22].
All these methods are not exclusive and could be coupled (e.g.
use of graphical and statistical analysis methods to support itera- tive manual calibration, semi-automatic procedures coupling mathematical and heuristic manual methods).
Westphal and Lamberts [30] present a manual calibration method that is applied to the EnergyPlus and combines energy audit techniques and sensitivity analysis. The main advantage of this sequential approach is the integration of a simple sensitivity analysis into the calibration process. The approach does not follow the classical way to perform energy audit and studies building base loads before its envelope and HVAC system. Moreover, numerous hypotheses (on the building and the equipment) are needed at the beginning of the calibration process and make the methodol- ogy not easily reproducible. The proposed calibration methodology developed by Raftery et al.[14]was applied to the EnergyPlus and is based on three core principles: (a) use of a detailed building energy simulation model that represents the real building as clo- sely as possible; (b) application of a reproducible and scientific method; (c) obtain and use sub-hourly sub-utilities level measure- ments from energy monitoring systems (EMS) and building auto- mation systems (BAS).
This classical manual iterative calibration methodology includes measurements issues and tries to keep the calibration process as systematic and ‘‘evidence-based’’ as possible. The cali- bration method developed by Raftery et al.[14]has been applied to a real office building but, unfortunately, no sensitivity issues have been included in the calibration process. Reddy and Maor [16]developed a calibration procedure based on the DOE2 model and using only monthly billing data. This calibration procedure involves heuristic steps (definition of a set of influential parame- ters basing on walk-through audit and inspection), advanced mathematical methods (Monte–Carlo method, use of optimization algorithms. . .) and numerous trials and simulation runs. This sophisticated and partially ‘‘black-box’’ calibration method allows identifying a subset of most plausible solutions to this under- determined problem. However, this method is also heavy to handle due to the multiple steps and can be confusing for non-experi- mented users[29].
Liu and Henze[15], developed a methodology to improve cali- bration accuracy of building energy models in an effort to improve predictive optimal control of active and passive building thermal storage. Integrating a global optimization algorithm with a build- ing simulation program, Liu and Henze[15]developed an optimi- zation environment to calibrate whole-building energy models, avoiding the repetition of manually adjusting individual model parameters. Yoon and Lee[17], developed a systematic process to calibrate building energy models of high-rise commercial build- ings using monthly utility data. The building used in this study was a 26-story commercial building located in Seoul, Korea, and was modelled in DOE-2.1E with electric cooling and gas heating. Error analysis was similar to Liu and Henze[15], and used the RMSE and the coefficient of variation (CV) of the root-mean-square error approaches (dividing the RMSE by the average measured value during a period of time) to minimize uncertainty in the model.
The next section will present the calibration methodology applied in this research work.
3.2. Present research work on calibration process
As a staring methodology, the calibration process used in the present work is based on past researchers such as Raftery et al.
[14]and Bertagnolio[18]. Two levels of calibration can be consid- ered depending on the availability of building, system and mea- surement data. TortoiseSVN[31], is the version control repository of the calibration process.
The two levels have been defined according to the classification proposed by Raftery et al.[14], Reddy and Maor[16]and Bertagno- lio[18]are presented as follow:
First level corresponds to ‘‘as-built’’ model of the installation.
This version of the model is based on available as-built data (plans, schemes and nameplate data of main HVAC components. . .) and will be used for parameters screening. During this step the version control software to track the revision history of the calibration pro- ject is used. On-site inspection (e.g. complete inventory of installed appliances and lighting powers) and BEMS analysis (setpoints, schedules. . .). The data collected at this stage correspond to the information that can be expected when proceeding to an energy audit/inspection of an installation. A summary of first level is as follow:
This step includes the collection of all readily accessible data about the building. A recommended list of sources but not exhaustive includes: as-built drawings and commissioning doc- umentation; Operation and Maintenance (O&M) manuals; com- plete geometry description.
An initial whole building energy model will be created at the design stage and is available as a starting point for the calibra- tion process.
A historical weather file was created from measured 2011 data obtained from the on-site local weather station in ERI building.
Maximal number of occupants and ‘‘typical’’ schedules for occu- pants and systems.
Nominal powers for main HVAC system components (heat pump, boiler and AHUs).
Detailed envelope composition. Material properties datasheets, architectural and mechanical as-built drawings, architectural elevation as-built drawings, wall materials and constructions taken from as-built drawings, official construction project scope document, any other mechanical as-built drawings, zone typing which yields detailed floor plain.
As-built electrical drawings and panel schedules were used to allocate these loads on a floor area weighted basis, office occu- pancy counts, electrical panel schedules, any other electrical equipment plug load consumption data not provided by the as-build was measured.
Survey of installed lighting and appliances loads densities.
Complete inventory of installed powers of auxiliaries (pumps and fans).
Analysis of the BEMS, the control low implemented and obtains more accurate information about the indoor setpoints and HVAC system operation.
Second level involve an intensive use of BEMS records and the monitoring data collected on-site by means of the measurement equipment (see below). At this stage, the probability ranges depend on the accuracy of the sensors, loggers and recorders.
In addition, level 2 include the information derived from the analysis of the answers to the Surveys and interviews. A summary of the second level consist on the collection of the following measurements:
Heat pump electricity consumption.
Boiler and pumps electricity consumption.
Underfloor heating thermal energy released.
Hot water temperature and flow rates.
Heat produced by the thermal solar panels.
Secondary and primary HVAC components electricity consump- tion measurement. All equipment characteristics should be ver- ified by commissioning documentation such as Operation &
Maintenance manuals.
Surveys and interviews.
G. Mustafaraj et al. / Applied Energy 130 (2014) 72–85
Indoor temperatures collected at hourly sampling rates are used as a zones temperature schedule into EnergyPlus.
Successively,Fig. 3-1 presents an overview of the calibration process which includes levels 1 and 2 that was firstly developed by Bertagnolio[18], and then further developed by the present authors. Level 1 (as-built input file) is used to run a first step sim- ulation and the accuracy of the calibration process during this stage can be unsatisfactory. The calibration process during level 2 is a more advanced step which consists of an iterative process to identify the most influential parameters. After having identified as a critical parameter, the value of the concerned parameter has to be estimated/refined. Various direct or indirect measurement techniques can be used for that purpose (e.g. direct indoor or sup- ply temperature measurement or indirect estimation of the operat- ing profiles by means of short-term monitoring of some lighting or appliances consumption measurements). When short-term (typi- cally daily or weekly) measurements are performed, collected pro- files (e.g. electrical load profile) can also be compared to the simulation results in order to check the validity of the model on an hourly (or sub-hourly) basis. The iterative process involved dur- ing level 2 will not be further described in this research work, and for more information see Bertagnolio[18]. The accuracy of the cal- ibration is evaluated by computing the classical calibration criteria in terms of MBE and CV(RMSE) calculated on an hourly and monthly basis for year 2011 (see Eqs.(3-1)-(3-3)). Past researchers such as Yoon et al.[32]and Pan et al.[33]recommended to use MBE and CV(RMSE) to evaluate calibration accuracy.
MBE¼
PNP
i¼1ðMiSiÞ
PNP
i¼1Mi
ð3-1Þ
CVðRMSEÞp¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
PNP
i¼1ððMiSiÞ2=NPÞ q
MP
ð3-2Þ
MP¼
PNP
i¼1Mi
NP
ð3-3Þ
whereMiand Siare measured and simulated data at instance i, respectively;pis the interval (e.g., monthly, weekly, daily & hourly);
Npis the number of values at intervalp(i.e.,Nmonth= 12,Nday= 365, Nhour= 8760) andMpis the average of the measured data. ASHRAE Guideline 14[34]gives the acceptable limits for calibration to hourly data as 10%6MBEhourly610% and CV(RMSE)hourly630%, and monthly data as5%6MBEmonthly65% and CV(RMSE)monthly615%.
Line graphs and bar charts are used to graphically compare simulated energy use to hourly sub-metered utility data, using two-dimen- sional time series plots and monthly percentage difference calculations.
Percentage difference calculations were conducted to deter- mine the accuracy of the model. To make graphical comparisons, MATLAB tool was developed to view calibration results. Finally, the next section will present the results analysis of the calibrated procedure applied throughout levels 1 and 2.
3.3. Calibration results analysis
The build-up of the floors, roof, external facades, internal parti- tions and windows of each floor were constructed from as-built structural drawings.Table 3-1present their thermal properties, where all of the material properties were taken from CIBSE[35]
and ASHRAE[36]. The as-built information were quite complete and allowed identifying relatively accurate ‘‘best-guess’’ values of envelope components characteristics (e.g.U-values). The remain- ing uncertainty is related to the potential differences between the as-built file and the realization (presence of thermal bridges. . .). The selection of the most influential parameters is based on the results of the sensitivity analyses performed in Ener- gyPlus. In general the main building occupancy is assumed to open 5 days a week, from 8.00 AM to 6.00 PM. The building is also likely to open at weekend and outside normal hours due to the nature of research and occupancy surveys. It is clear from occupancy surveys that the building has not been fully occupied in a number of areas currently.
This estimated to be of the order of 50% in laboratory and open plan areas in general while large areas of the Lower Ground Floor
Fig. 3-1.Calibration methodology (adapted from Bertagnolio[18]).
laboratories appear not to be in regular use. A survey of the build- ing indicates that the internal rooms on all floors, which are mostly storage depositories of one form or the other are empty and have no significant equipment in them with the exception of the Weigh- ing Room. The lighting, people and equipment loads associated with these areas are assumed to be off. For the people the activity of each occupant is considered to ‘‘light work’’, for a typical office application.
In the offices and other designated areas each person is assumed to have a computer and 90 W sensible gain has been allowed for each workstation. This is generally considered an aver- age figure from an ASHRAE[25]. The equipment assumes Pentium based technology where the monitor size considered was 17-inch (seeTable 3-2). Computer load profiles follow the occupant profiles for each space.Table 3-2summarize the internal heat gains inside laboratories and offices based respectively on detailed survey, as- built electrical drawings, ASHRAE[25]and CIBSE[35]. Both values and uncertainty ranges of lighting and appliances power densities were identified with a good accuracy.
Infiltration, or incidental air leakage through building enve- lopes, is a common phenomenon that affects both indoor air qual- ity and building energy consumption. The building underwent an
air pressure test and was found to achieve the design specification of 5 m3/h/m2at 50 Pa [37]. A value of 0.15 air changes per hour (ACH) at a wind speed of 4 m/s was taken for this type of building.
The model is configured to linearly increase the infiltration levels to the building with increasing wind speed. Infiltration has been applied to external areas and circulation areas only with internal areas assumed to have negligible infiltration levels. Tables 3-3 and 3-4outline the properties of AHUs taken from Air System Flow Rates Commissioning Report. It was possible to gain access to the BMS scheduling for the AHUs, and Table 3-5 present the AHUs schedule. Table 3-6 give an overview of the properties of the pumps taken from Operation & Maintenance manual. For more information related to their position into HVAC plants seeFig. 2- 3. The remaining main parameters are summarized inTable 3-7.
Next section it will be presented the results analysis related to electricity, heat output and gas consumption obtained throughout the level 1 of calibration process.
3.3.1. First level of calibration
The first level of calibration includes as-built information with realistic occupancy/operating profiles and actual building use and operation as given in the previous section throughTables 3-1–3-7.
This could mean that implementing complete as-built information with realistic occupancy/operating profiles and detailed informa- tion about building use/operation into the model leads to a good representation of the final energy consumption of the building.
Table 3-8presents the variation of MBE and CV(RMSE) between a Minimum (Min.) and a Maximum (Max.) throughout year 2011, based on hourly and monthly calculation. It can be notice that the classical criteria (Section3.2) computed hourly, and monthly for heat pump electricity consumption, heat pump heat released, build- ing total electricity consumption, natural gas consumption and indoor room temperature is not fully satisfied (Table 3-8). Thereby, the second level of calibration is required to further improve the quality of the models. Figs. 3-2 and 3-3 present a comparison between hourly model output and real measurements. The accuracy of the results related to January 2011 is taken as an example. Similar Table 3-1
Construction element values and uncertainty ranges.
Construction element U-value (W/m2K)
Build up Location in building
Lower ground floor slab 175 mm RC Slab
0.452 750 mm clay, 150 mm stone, 175 mm Concrete Slab, 50 mm insulation, 75 mm Screed
Lower ground floor slab Ground floor slab first floor slab 0.1 250 mm Concrete slab, 275 mm polystyrene void former, 75 mm Screed Ground floor and first floor slabs Internal partition 0.498 25 gypsum plaster, 50 mm cavity, 50 mm glass fibre quilt, 15 mm plywood,
10 mm gypsum plaster
Internal partitions External insulated 250 mm RC Wall 0.258 250 mm RC, 100 mm polystyrene, 25 mm gypsum plaster East, West face Timber clad external insulated
surface
0.848 10 mm hardwood, 40 mm Rockwool, 15 mm Plywood South face
250 Timber clad insulated surface 0.839 25 mm Rock wool, 38 mm Air Gap, 250 mm cast concrete, 15 mm hardwood
North face Flat roof 0.104 10 mm stone chippings, 20 mm felt/bitumen layer, 75 mm screed, 275 mm
polystyrene, 250 mm concrete slab
General flat roof
Glazing 1.7 4 mm Optifloat/16 mm/4 mm k glass North, South, East and West
Facades
Table 3-2
Internal heat gains values and uncertainty ranges.
Location Occupant
density (m2/person)
Lighting loads (W/m2)
Computer equipment (W/m2)
Additional equipment (W/m2)
Open plan offices 8 8 11.25 2
Small offices 12 8 12 2
Laboratories 6 12 15 20
Open computer labs 8 8 11.25 2
Meeting rooms 5 8 4 2
Circulation N/A 8 N/A N/A
Toilets N/A 7 N/A N/A
Stairs N/A 3 N/A N/A
Table 3-3
AHU supply & return power.
AHUsproperties AHU 01 AHU 02 AHU 03 AHU 04 AHU 05
Supply (m3/h) 1206 450 792 3963.6 2016
Pressure (Pa) 630 603 630 1167 640
Impeller efficiency (%) 68 68 60 74 72
Absorbed power (W) 220 110 230 1740 500
Including losses (W) 330 170 340 2730 780
Overall system efficiency (%) 40 46 40 47 46
G. Mustafaraj et al. / Applied Energy 130 (2014) 72–85
accuracy is obtained throughout the other months. Consequently, to avoid any repetition the graphs related to the other months are not shown. The data related to building total electricity consumption and natural gas were not available at hourly rate thereby no results are shown. Similarly, monthly values for MBE and CV(RMSE) related to indoor dry bulb temperatures are not presented.
Monthly total electricity consumption and natural gas were available from bills, where an analysis of calibration accuracy is shown inTable 3-8. The results analysis related to monthly calibra- tion accuracy for building total electricity consumption and natural gas are shown in Section3.3.2(seeFigs. 3-6 and 3-7).
3.3.2. Second level of calibration
Temperature and humidity have been collected throughout BEMS in different areas of the building in order to determine the level of comfort achieved in occupancy areas. BEMS monitored HVAC components in order to determine their power consumption and their operation. This data was collected for a period of one year at an hourly sampling rate. In addition, operation schedules of arti- ficial lighting and quantification of the actual plug loads densities and schedules are also been analysed in more detail throughout this level. Finally, a survey was undertaken and about 60 occupants answered it. This approximately corresponds to half of the occu- pants of the building. Attention was paid to the questions related to holidays. Based on the survey results, it is estimated that occu- pants take, in average, about 15 days off during summer (i.e. three weeks). These holidays are supposed to be equally distributed over July and August.
The occupancy and operating factors of offices lighting and appliances have been adapted accordingly. Legal holidays (such as Eastern, Labour Day, and Bank holidays) were also integrated into the model. During these days the building is operated in
‘‘weekend mode’’. Table 3-9 present the variation of MBE and CV(RMSE) between a Minimum (Min.) and a Maximum (Max.) after applying the second level of calibration process, and based on hourly and monthly calculation. Throughout this stage the clas- sical criteria (Section3.2) is mostly satisfied except few cases. A comparison between measurements and model output related to the first and second level of calibration is presented in Figs.
3-4–3-7. Results analysis demonstrate an improvements of model prediction going from first to second level of calibration process.
During the first level of calibration fixed values of temperatures (seeTable 3-7) were taken from ASHRAE[37]and then used as schedule into EnergyPlus. Contrary to first level, during the second level of calibration real values of zones temperature were collected from BEMS at an hourly sampling rate and used in EnergyPlus as schedule for the temperature. Due to numerous zones that are present the results analysis related to indoor temperatures of five zones are shown only. These zones correspond to room LG04, open plan office spaces 1.23 and G24, and laboratories G05 and 1.04 (seeFig. 2-2). A comparison between EnergyPlus model output temperatures obtained from the first and second level of calibra- tion, and real measurements are presented throughout Figs.
3-8–3-12.
The analyses covered all year round, but due to space con- straints, are presented only two weeks of results (1–16 January Table 3-4
AHU supply & return power cont.
AHUsproperties AHU 01 AHU 02 AHU 03 AHU 04 AHU 05
Pressure (Pa) 555 468 479 454 439
Impeller efficiency (%) 65 60 63 75 75
Absorbed power (W) 260 140 190 380 330
Including losses (W) 380 210 280 600 510
Overall system efficiency (%) 44 40 42 48 48
Total power (W) 700 400 600 3300 1300
Specific fan power (W/m3/h) 0.78 0.69 0.75 1.05 0.64
Table 3-5 AHUs schedule.
AHUs Weekdays Weekend Served areas (seeFig. 2-2)
AHU 01 8–18 h 9–13 h Saturday, of Sunday Lobby and Toilets (LG21, G10, G20, G29, 1.2 and 1.32)
AHU 02 On continuously On continuously Tea station /Comms/Weighing Room (G19, G34, G36 and 1.18)
AHU 03 On continuously On continuously Chemical store LG19
AHU 04 8–18 h Off Cleanrooms (1.15, 1.16 and 1.17)
AHU 05 8–18 h Off General storage (LG13, L16, LG19, G14, G15, G17, G18, 1.12 and 1.13)
Table 3-6 Pumps properties.
Pump ref. Service Duty Pressure Power (W) Speed
m3/h Drop (Pa) Consumption
P1 Aquifer 36 250,000 3000 Variable
P2 Aquifer 36 250,000 3000 Constant
P3 Aquifer 3.6 250,000 550 Variable
P5 Cooling circuit 3.6 130,000 370 Constant
P6 Solar panels 5.04 110,000 370 Constant
P7 Boiler primary 33.68 160,000 1500 Constant
P8 Underfloor heating 32.4 260,000 4000 Variable
P9 Heating coils 7.2 180,000 4000 Variable
P11 DHW circulation 0.72 90,000 600 Constant
P13 Heat exchanger HE-04 14.4 80,000 1000 Constant
P14 Heat pump HP-01 19.8 95,000 1550 Constant
2011) are presented in this paper. Results analysis shows that throughout the second level of calibration there is an improve- ment of models in predicting the indoor temperature
(see Table 3-9). Similar calibration accuracy was obtained throughout the remaining zones and other periods of year 2011.
3.4. Opportunities of energy efficiency
After completing the calibration process, this section will deal with the actions performed to further reduce the energy consump- tion, focusing on the energy savings that can be achieved by modify- ing the time schedule of the heat pump. Other savings such as lightings and AHUs will be subject of further research work in the future. In reality, the heat pump is turned ‘‘ON’’ during night time where the electricity price is available at cheaper rate compared to day time. Underfloor heating system is positioned inside a cement screed structure with a thickness of 0.075 m.
The time scale which heat pump is turned ‘‘ON’’ can vary between 6 and 12 h and dependent on the weather conditions. This is managed by the technician of the BMS, which based on his expe- rience and weather forecast conditions decides in advance how many hours will turn ‘‘ON’’ for in the following week. Conse- quently, the heat pump which supplies 70% of heat to the building is not managed correctly by the present weather condition and real thermal behaviour of the building.
Alternatively, present research analysis used EnergyPlus to turn
‘‘ON’’ and ‘‘OFF’’ the heat pump based on the real thermal behav- iour of building and weather condition supplied by weather data file. In the present work the comfort conditions included into Ener- gyPlus are based on ASHRAE guide lines[37].
The temperature during the day in the areas subject to natural ventilation (70% of the building areas) is regulated by automated windows scheduled to open at 20°C of the indoor temperature.
Results analyses have shown that the time required to keep ‘‘ON’’
is from 4 to a maximum 8 h at night time for maintaining satisfac- tory comfort conditions inside the building. Consequently, less time is required compare to that managed by the technician to the BEMS (from 6 to 12 h).
Table 3-7
Other input parameters values.
Unit Description Value
°C Meeting rooms – temperature setpoint 20
°C Open computer labs/laboratories – indoor temperature setpoint
20
°C Open plan offices/small offices – indoor temperature setpoint
21
°C Toilets, stairwells and circulation – indoor temperature setpoint
18
°C Corridors – indoor temperature setpoint 18
°C Cold rooms – indoor temperature set point 14
°C Natural ventilation motorized windows opening – indoor temperature
22
°C Natural ventilation motorized windows closing – indoor temperature
20 Tank rooms and other unoccupied areas – Indoor temperature
Free floating
ACH Average ventilation rate range 3–5
°C Boiler hot water temperature range 70–80
W Boiler nominal power 162,000
g Boiler efficiency 0.95
W HeatPump heating capacity 30,000
W HeatPump compressor power input 7500
W/m2 Underfloor heating output 90
Hours Underfloor heating operation time 4–15 m2 Flat plate – gross area per collector 2 m3/h Flat plate – water flow rate per collector 0.137
% Flat plate – solar collector optical efficiency 84 m3/h/m2 Chilled beam supply air volumetric flow rate 5 m3/h Chilled beam total chilled water flow rate 0.18 m3/h Fume hoods – air flow rate range 350–1150
g Fume hoods – fan efficiency 0.5
Hours Daily occupancy time 10
Hours Lighting daily operation time 10
Hours Appliances daily operation time 10
Table 3-8
First level – calibration accuracy.
First level% January–December 2011
(MBE)hourly CV(RMSE)hourly (MBE)monthly CV(RMSE)monthly
Min. Max. Min. Max. Min. Max. Min. Max.
Heat pump electricity consumption 6.8 10.3 15.3 33.6 2.7 8.3 8.5 16.1
Heat pump heat released 7.4 18.7 20.5 41.7 2.3 4.5 5.5 12.5
Building total electricity consumption N/A N/A N/A N/A 5.5 8.6 8.7 20.7
Natural gas consumption N/A N/A N/A N/A 2.2 6.1 6.3 17.5
Indoor zone temperature 9.1 15.3 21.3 36.2 N/A N/A N/A N/A
Fig. 3-2.Hourly heat pump electricity consumption comparison.
G. Mustafaraj et al. / Applied Energy 130 (2014) 72–85
Fig. 3-13presents heat pump monthly electricity consumption between real measurements and that obtained by EnergyPlus model output. It is verified that savings can vary between 20%
and 27% on monthly based.Fig. 3-14shows a comparison between
measurements and model output related to heat pump heat released, where from 5% to 10% less heat is released compared to that in reality. Finally,Fig. 3-15gives an example of a comparison Fig. 3-3.Hourly heat pump heat released comparison.
Table 3-9
Second level – calibration accuracy.
Second level% January–December 2011
(MBE)hourly CV(RMSE)hourly (MBE)monthly CV(RMSE)monthly
Min. Max. Min. Max. Min. Max. Min. Max.
Heat pump electricity consumption 5.6 7.5 7.3 25.1 1.5 5.6 5.7 13.3
Heat pump heat released 6.2 10.3 18.2 33.5 1.8 3.6 5.4 10.6
Building total electricity consumption N/A N/A N/A N/A 3.8 6.1 6.3 16.5
Natural gas consumption N/A N/A N/A N/A 1.7 5.7 4.5 14.1
Indoor zone temperature 6.5 11.4 12.4 28.7 N/A N/A N/A N/A
Fig. 3-4.Monthly heat pump electricity consumption comparison.
Fig. 3-5.Monthly heat pump heat released comparison.
Fig. 3-6.Monthly whole building electricity consumption comparison.
Fig. 3-7.Monthly boiler gas consumption comparison.
between real measurements and model output for the heat pump COP.
Results analysis shows an increase in the values of COP that varied between 2.6 and 4.82 compared to that of the real
measurements, which are between 2.4 and 4.2. Similar results were obtained for the heat pump COP throughout the remaining months, but to avoid any repetition, the relevant graphs were not shown in this research.
Fig. 3-8.Hourly open office first floor 1.23 indoor temperature comparison – two weeks data.
Fig. 3-9.Hourly open office ground floor G24 indoor temperature comparison – two weeks data.
Fig. 3-10.Hourly laboratory first floor 1.04 indoor temperature comparison – two weeks data.
Fig. 3-11.Hourly laboratory G05 indoor temperature comparison – two weeks data.
G. Mustafaraj et al. / Applied Energy 130 (2014) 72–85
4. Conclusions and future works
The purpose of this research work is to present a methodology that starts with calibration and then continue with the necessary actions for improving hourly electricity consumption related to a
water to water heat pump. The calibration process consists of two levels, where hourly and monthly EnergyPlus model output related to heat pump electricity consumption, heat pump heat released, building total electricity consumption, natural gas and indoor zone temperatures were compared to them related to real measurements.
Throughout the first and second levels of calibration process the values of MBE and CV(RMSE) on hourly and monthly based analy- sis varied within the following ranges:
First level of calibration:
(MBE)hourlyvaried between9.1% and 18.7% and CV(RMSE)hourly
varied between 15.3% and 36.2%.
(MBE)monthly varied between 5.5% and 8.6% and CV(RMSE)monthlyvaried between 5.5% and 20.7%.
Second level of calibration:
(MBE)hourlyvaried between 6.5 and 11.4 and CV(RMSE)hourly
varied between 12.3% and 33.5%.
(MBE)monthly varied between 1.5% and 6.1% and CV(RMSE)monthlyvaried between 4.5% and 16.5%.
After the second level of calibration process the classical criteria is almost satisfied (Section3.2) except only for few cases. Due to the complexity of HVAC plants involved the accuracy of calibration process could not be further improved. The second part of this research work presents the necessary actions to improve the elec- tricity consumption related to water to water heat pump.
In reality the time schedule of turning ‘‘ON’’ and ‘‘OFF’’ the heat pump is fixed by the technician who uses their experience and con- sequently does not depend on the real thermal behaviour of the building and weather conditions. Contrary, the present research work calculates the effective heat pump schedule time as a result of simulation conducted by EnergyPlus tool. EnergyPlus calculates the real thermal behaviour of the building based mainly on build- ing structure, local weather data file and natural ventilation. The time schedule of turning ‘‘ON’’ the heat pump varied between 4 and 8 h at maximum, while that managed by the technician is from 6 to 12 h. Thereby, savings of heat pump electricity consumption can vary between 20% and 27% on monthly bases.
Further research work is required to better control the HVAC plants according to Operation & Maintenance manual. This is Fig. 3-12.Hourly room LG04 indoor temperature comparison – two weeks data.
Fig. 3-13.Monthly heat pump electricity consumption saved.
Fig. 3-14.Monthly heat pump heat released comparison.
Fig. 3-15.Hourly heat pump COP comparison.
