Research Article  Open Access
Almahdi AbdoAllah, M. Tariq Iqbal, Kevin Pope, "Energy Consumption Analysis of a Large Building at Memorial University", Journal of Energy, vol. 2019, Article ID 5243737, 21 pages, 2019. https://doi.org/10.1155/2019/5243737
Energy Consumption Analysis of a Large Building at Memorial University
Abstract
In this paper, energy consumption analysis and a process to identify appropriate models based on heat dynamics for large structures are presented. The analysis uses data from heating, ventilation, and airconditioning (HVAC) system sensors, as well as data from the indoor climate and energy software (IDA Indoor Climate and Energy (IDAICE) 4.7 simulation program). Energy consumption data (e.g., power and hot water usage) agrees well with the new models. The model is applicable in a variety of applications, such as forecasting energy consumption and controlling indoor climate. In the study, both dataderived models and a greybox model are tested, producing a complex building model with high accuracy. Also, a case study of the S. J. Carew building at Memorial University, St. John’s, Newfoundland, is presented.
1. Introduction
Heating, ventilation, and airconditioning (HVAC) systems are crucial for indoor climate management and air quality. These systems are also a key factor in overall operational costs. For industrial buildings, nearly onethird of the energy usage depends on HVAC system operation [1–3]. The recent rapid industrialization of the world’s developing nations has led to an increase in energy demand, followed by a rapid rise in pollution levels. As a result, researchers are investigating ways to mitigate or prevent further environmental damage through a combination of conservation methods and widescale adoption of renewable energy systems [4].
Ideally, HVAC (heating, ventilation, and airconditioning) systems are developed to form an interior environment that provides usercomfort with operational costefficiency. To maintain consistent usercomfort and affordability amidst changing variables, a suitable control system is needed. Several options have been modeled. One popular method uses data to create a mathematicalbased HVAC system that considers input and output variables to find and set system parameters. Datadriven HVAC can readily identify strategies for system refinement and enhancement. These types of model determination are termed system identification (SI) in the literature (ASHRAE, 2005) [5].
In previous studies, researchers categorized modeling approaches into two main types, namely, black box and grey box. For the black box method, no prior information is required, but for the greybox strategy, there must be a reservoir of preexisting knowledge. Due to these constraints, the black box modeling approach is generally better represented in the literature. Examples of black box models applied to HVAC systems are polynomial forms such as ARX, ARMAX, BJ, and OE. Despite its popularity, the black box model strategy overlooks the physical features of a system, leading to issues around the practical application in realworld designs.
For example, ChiMan Yiu et al. [6] looked at black box strategies for airconditioning systems. The researchers contrasted two ARMAX models: the first, a singleinput/singleoutput system, and the second, a multiinput/multioutput (MIMO) system. For the MIMO system, ChiMan Yiu et al. [6] employed parameters derived from the recursive extended least squares method. Mustafaraj et al. [7] investigated temperature and humidity models (ARX, ARMAX, BJ, and OE) for office environments, using a black box approach. The same researchers [8] continued their work by applying nonlinear autoregressive models with NARX inputs to gauge temperature and humidity levels while comparing and contrasting the outcomes for these models with those of linear ARX models. Additionally, Mustafaraj et al. [8] examined CO_{2} concentrations’ effect on model performance, considering that occupancy levels in a building are directly correlated to CO_{2}. Rabl [9] provided a summary of approaches applied for dynamic analysis of power usage by modeling heat dynamics. These models were applied in studies by Sonderegger [10] and Boyer et al. [11], using differential equations. For dynamic models, parameter estimation and system identification are essentially the same processes.
This paper uses the greybox method for modeling dynamic systems. This approach, which is accurate and comprehensive, enables the collection of information on a structure’s thermal properties [12–14]. Greybox models employ discretetime measurement equations and continuous time stochastic differential equations. An HVAC system’s yearly power usage can be predicted using energy performance analysis tools, such as SIMBAD, EnergyPlus, eQUEST, HVACSIM+, IDAICE, and TRNSYS at set time frames (hourly or less) by a set of equations describing a building’s thermal performance. Calculations comparing various design options are usually made for partload and fullload performance [15–18].
This paper simulates a whole building (the S. J. Carew building in St. John’s, Newfoundland) using the IDA Indoor Climate and Energy (IDAICE) 4.7 simulation program. In addition to examining the modeled structure’s power use, the study investigates a 3D model, a heat model (with variable parameters), and an IDAICE model library. The IDAICE was developed to investigate different thermal climate zones occurring in indoor environments [19].
By actual details and logged data of a large building in a cold climate, we used the IDAICE software and building logged data to model the building in IDAICE software. We propose 12 inputs and 12 outputs dynamic model for the system. The dynamic model is required to design and test system controllers before actual implementation. To determine a statespace system model, we use MATLAB system identification toolbox. For the model determination, we used data from IDAICE software. Contributions of this paper are building data, proposed system dynamic model, a method to determine the system model, and developed system dynamic model parameters. The primary objectives of this paper are as follows:(1)Apply the IDAICE program to model the S. J. Carew building (Memorial University, St. John’s, Newfoundland) using all real dimensions and building materials information from Department of Facilities Management and the Honeywell office, which is responsible for running and monitoring the system.(2)By using the IDAICE software we can divide the single valve of hot water coming from the main room to four distinct units, enabling each airhandling unit (AHU) to have an individual valve for control the mechanical hot water flow for each zone as another input of the system are supply fan speed and fresh air dampers position.(3)Compare the data from the IDAICE program with the building logged data for validating the power use outcomes.(4)Determine the potential of applying the system identification approach to reduce the time needed to simulate the building and use the system model simulation results in identifying the dynamics related to a building’s climate control.
2. The Building for This Case Study
A case study on the S. J. Carew building, with an interior size of 25,142 m^{2}, is conducted. The building is located on the campus of Memorial University, St. John’s, Newfoundland, and includes several teaching rooms and research labs for the Memorial’s Faculty of Engineering and Applied Science. The building also features a large cafeteria. There are four individual airhandling units (AHUs) in 300 zones within the building. Figure 1 illustrates a 3D model for the structure, applying the IDAICE program mentioned in the previous section, while Table 1 provides an energy report.

3. Simulation Tool
The S. J. Carew building is modeled by employing IDAICE as a dynamic thermal simulation tool. This program is selected because it is widely accepted as a viable thermal building performance simulator towards the study of power usage and indoor thermal climate of whole buildings [19]. The IDAICE program uses symbolic equations framed in a modeling language and a variable timestep differentialalgebraic (DAE solver). The models can be expressed through the Neutral Model Format (NMF)/Modelica and act as both computer code and readable document, which are applicable to various simulation environments [20, 21].
The simulation tool IDAICE 4.7 is employed to predict the power usage and interior climate of the S. J. Carew building. The IDAICE 4.7 tool is ideal for modeling of multiplezone HVAC systems as in the S. J. Carew building. IDAICE 4.7 is able to determine the general thermal comfort level of the building by measuring the internal air quality (IAQ) and performing dynamic simulations. The heat exchanger uses controllers to maintain zonal temperatures, which can be set as fixed points by modulating control valves. Meanwhile, in the real system (as shown in Figure 2), a hot water valve collects relevant data for water heated by a heating coil. Although the building’s system features a single valve for its hot water production, the IDAICE software divides the single valve into four distinct units, enabling each airhandling unit (AHU) to have an individual valve [22, 23].
4. Building Model
IFC files were used to develop a simulation model from the building information model (BIM). As shown in Figure 3, the third floor of the building features an AHU and all floors in the building have their own individual AHU.
Data from the Facilities Management Department at Memorial University are used to source construction information regarding building dimensions and elements such as windows, doors, and walls. The data then are inputted to the IDAICE program.
Figure 4 depicts eight different kinds of windows used in the Carew building, while Figure 5 illustrates the building’s south elevation. Heating system information (e.g., radiator type and position) is presented in Figure 6, and Figure 7 depicts the main room’s ventilation system for AHU_{1}, AHU_{2}, and AHU_{3}. Each of the areas has individual internals loads (i.e., occupancy and light based on floor type and usage) that have been determined by applying national building code monthly values. The ventilation design determines the supply and exhaust air flows, with standard commercial building pressure coefficients applied. There are doors dividing nearly all of the areas in the structure, enabling bidirectional air flow (even through closed doors). Air tightness is measured as an n50parameter, while infiltration and exfiltration are simulated using the IDAICE air flow network feature.
5. Simulation Results
The IDAICE 4.7 program was used to analyze energy use in the S. J. Carew building at Memorial University, St. John’s, Newfoundland. The analysis involved a number of factors, such as weather data, infiltration, external/internal heat gain, and overall heat capacity. The simulation was done for the course of one full year (January 1, 2016, to December 31, 2016). The space heating and total energy consumption analysis results for the building in five points as following.
5.1. AHU Results
(i) AHUs Temperature. Figure 8 shows AHU_{1} supply air and return air, as well as the outdoor air temperature. As can be seen, the air temperature represents a mixture of temperatures from individual zones, while the air supply temperature represents the temperature of the air terminal zone following any alterations made to the duct or fan systems. The supply air temperature setpoint refers to the air temperature prior to these alterations.
(ii) AHU Airflow. The flows represent the total flow from every zone impacted by AHU_{1} (Figure 9) and have been multiplied according to weight (i.e., how many zones are the same type). This value is included in every zone. The flow is volumetric and assessed for actual temperatures. Therefore, it can differ from the setpoint flow for each zone. Setpoint flows are determined from mass flows based on variations in density.
(iii) Heating and Cooling AHUs Coils. Figure 10 depicts the central cooling and energy for AHU_{1}. In circuits that are waterbased, the energy consumption can be measured in the circuit according to temperature changes and mass flow (i.e., supply/return). Therefore, heat can be calculated after generation losses are included, but prior to the calculation of emission and distribution losses. Cooling energy is included as a positive quantity in the calculations.
5.2. Heat Balance
Figure 11 depicts the zones’ sensible (dry) and full latent (moist) heat balance. To find the sensible heat balance only, the details for the zone’s power report need to be logged. In this setup, the control volume indicates airwetted surface area located at the room unit zoneside, backed by an air gap. Heat balance contributions can be allocated as shown in Figure 11.
(i) Equipment Heat. This type of heat emanates out of equipment like printers and computers as a form of radiant or convective heat.
(ii) Floor and Wall Heat. As the control volume is positioned directly below the surface of floors and walls, any measurement of heat indicates the presence of conductive heat passing through the structural element. This type of thermal energy can include net transmission and heat storage, in addition to internal heat functions (e.g., infloor radiant heat). The thermal energy that has been stored as a function of room masses, such as in furniture, also is in this category.
(iii) Daylight Heat. This type of heat describes sunlight streaming into open doors or through windows, taking into consideration any shortwave radiation which exists. Solar radiation that has been absorbed and retransmitted is excluded from this category.
(iv) Heating/Cooling Room Heat. This type of heat is represented by controlled room units (e.g., radiators or chilled beams). In hydronic systems, there is an automatic calculation to account for the radiation and convection aspects, as described in the manual. Floor heating is excluded from this category.
(v) Window Heat. This type of heat describes heat emitted from window surfaces, such as through retransmitted absorbed solar radiation or through conduction. Longwave radiation entering via openings such as opened doors falls under this category of heat. Solar radiation can have two major impacts on a room’s heat:(i)It can be absorbed by the window covering or pane and then emanate through the room as a radiative or convective process.(ii)It can be directly transmitted as shortwave radiation and be reflected by room surfaces until finally absorbed by internal room masses (furniture, equipment, people, etc.).
(vi) Air Flow Heat. This describes all air flows, including infiltration, flowing from other zones and mechanical ventilation.
5.3. Energy Delivered
The report on the energy delivered provides a general overview of the total energy purchased or generated in the S. J. Carew building, as shown in Figure 12. The reported items are matched to the energy meters. The report also shows the primary form of energy employed, as well as the cost and estimates for CO_{2} emitted. These are presented according to the structure’s floor area and with regard to absolute values. Conversion factors from the meter energy to other measures are given as energy meters.
5.4. Results: Energy from Systems
The results also provide a general review for HVAC system energy flow. As shown in Table 2, the review is in three categories: use energy, utilized free energy, and auxiliary energy. The results provide a means to validate real data and then apply this data for system identification to obtain the Carew building’s HVAC system’s statespace model.

5.5. Results: Energy from Zones
The results provide information on the sensible heat balance in the Carew building’s zones. Information on total (i.e., dry and wet) heat balance, are presented in the heat balance Figure 6. The data are provided for monthly and oneyear (simulation period) basis. Figure 13 provides details on envelope transmission losses, with control volume being positioned at the surface of the floor as well as on the ceiling and inside walls. For slab (embedded) cooling and heating processes, the control volume also involves activated layers and thus includes large thermal masses.
6. Simulation Validation for IDAICE
A viable model must provide accurate results and also meet the required specifications. In the present work, the building data used was provided by Memorial University’s Department of Facilities Management and the Honeywell Office. The data provided in Tables 3 and 4 (energy and hot water consumption) for the S. J. Carew building were collected between April 2012 and May 2017, inclusive. These were used to compare, and contrast power consumption derived from real data with power consumption derived from the IDAICE program data.


The first step for comparison was to verify design details for the Carew building. These details included aspects such as building materials, location, dimensions, total area, etc. The second step was to make a comparison using the file for outdoor air temperature/weather as represented in the IDAICE program (based on readings from St. John’s Airport, [ASHRAE, 2013]) and the building’s actual outdoor air temperature obtained from the Honeywell software data. Figure 3 depicts the sensor (1.4° OA); the average of the temperature readings from 2016 in onehour time samples for both data was the same.
A viable model needs to have both accurate results and the ability to satisfy any required specifications. Figure 14 shows the IDAICE model of hot water usage from January to December 2016. The energy consumption for hot water was more than 800,000 kWh in Jan and Dec. Also, it was almost 300,000 in the summer time (Jul and Aug). Furthermore, although the actual data for hot water usage measured slightly low in some months and slightly high in others, it compared well to the IDA simulated data. Regarding overall energy consumption, the modeled data are only somewhat different than the actual data. Figure 15 shows the actual (measured) data as moderately higher than the simulation data, but these slight differences could be due to discrepancies in the lab readings due to miscalibrated equipment.
7. System Identification
Our study used the IDAICE 4.7 simulation software for measuring the interior environment as well as the overall energy performance. This software is able to simulate and model multiplezoned HVAC systems and is also gauge interior air quality (IAQ), energy requirements, and thermal comfort levels. To model the S. J. Carew building, the zonal inputs and outputs must be included in the identification data. There are three main steps in system identification [24–27]:
(i) Collecting the data towards model identification.
(ii) Choosing an appropriate model structure.
(iii) Building a model that provides the best functionality (i.e., satisfies specifications and gives accurate results).
During these steps, the focus is on optimizing the chosen model to suit a reallife system. In this study, a structure is used that features four AHUs as a means for identifying the statespace model of the system. The data used for system identification were collected during the winter months, which means that the coldwater valve was not operating. Additionally, because the S. J. Carew building has four floors, the system features twelve inputs and twelve outputs overall, calculated from three inputs () and three outputs () per floor. Figure 16 illustrates the details.(1)Zonal Temperature °C () (). These data are derived from the IDAICE software. Although the actual system features sensors in every room, the temperature on each floor still needs to be measured. The data from the IDAICE software are used to control the hot water valve. Figure 17 illustrates the outputs.(2)Hot Water Valve for Heating Coil/Zones . These data are also derived from the IDAICE software. In the actual system (as shown in Figure 16), a hot water valve collects data on hot water use. Note that this system only has one valve for hot water production, whereas in the IDAICE software there are four valves, which enables every floor to have a separate valve. Figure 18 shows these inputs as percentage of opening and closing operation of the hot water valves.(3)Fresh Air Dampers . As shown in Figure 18, the fresh air sensors positioned in AHUs are able to gauge, in percentage, the amount of fresh air entering the building. The sample time used in these calculations is used in all data.(4)CO_{2} Levels (CO_{2}) (). This data is obtained from the sensors for return air flow ducts for individual AHUs. Figure 19 depicts CO_{2} levels occurring in AHUs. These outputs can be applied in moderating fresh air dampers.(5)Static Air Pressure () (). This data comes from two sensors: one for hot ducts and one for cold ducts. As illustrated in Figure 20, these outputs can be applied to the control of supply fan speed.(6)Supply Fan Speed . This data is derived from AHUs, the sensor. Figure 18 depicts the sensors measuring the fan speed of AHUs. The sample time for gathering the data is one hour, and the input signals are obtained in percentages.
System disturbances () can occur with changes to wind speed/direction and outside temperature. These changes are recorded in the IDAICE software and the input/output signal data series organized through MATLAB. The ordering of the data is imperative before moving onto the next stage, which is system identification using the System Identification (SI) Toolbox. Every individual data set is cut in two: onehalf represents estimation data, while the other half represents validation data.
Data from the first half (Data 1) are organized as input/output sets using MATLAB. The inputs are arranged into 12 columns, with every column relating to a specific input signal. Note that the number of rows is equal to the number of simulation period hours. Similarly, the outputs are also arranged into 12 columns, with every column relating to a specific output signal and the number of rows being equal to the number of input arrays. In these calculations, both the estimation and validation data have a 90day time frame. Figures 21, 22, and 23 show some of the time plots of this data for estimation. The CO_{2} level of AHU_{3}, static air pressure of AHU_{2} and zone temperature of AHU_{1} is shown. The output is the upper plot and the input is the lower plot.
Every statespace model is estimated in the SI Toolbox. The models undergo a comparison based on the degree of accuracy between the validation data sets’ estimated and measured (i.e., real) outputs. In comparison, the estimated and real outputs are plotted for every model, after which a numerical value is allotted regarding the model’s “fit.” Using the SI Tool, the estimated outputs for numerous models are able to be plotted quickly and at the same time, with the model showing the highest value (i.e., the best “fit”) deemed to have the greatest reliability. As an outcome of the process, we can obtain statespace models for Data 1 data groups and compare models across different seasons. For detrending the data, there are no alterations made to any relative differences among inputs and outputs.
Figure 24 shows the output of the model that follows the temperature of a zone in AHU_{1} with the same output as the real system. The agreement between these graphs can be seen as a percentage of the error. Ideally, this result is 57%. Also, Figure 25 shows that the system performance percentage for the estimated model and the actual system of CO_{2} level of zone 3 in AHU_{3} was 75%. Also, the percentage of the estimation model of the static air pressure the simulated or predicted model output is shown together with the measured validation data in Figure 26.
The part of the system data that the model could not describe is called the residuals [28–30]. They contain important information about the quality of the estimated model. The crosscorrelation between residuals and the correct model does not exceed the confidence level [28]. If this is the case, the original model has captured the underlying properties of the system. The remaining autocorrelation indicates whether the error pattern is accurate. Standard process models do not evaluate the error model and unknown interference is not in the original model, thus the remaining runtime is not used for model verification. Figures 27, 28, and 29 show plots of the autocorrelation and crosscorrelation of system responses to inputs. It is clear from the crosscorrelation diagram of these figures that the estimated model is very similar to the responses of the system to the inputs; the correlation curves lie between the dashed lines.
To determine model settings for the system, a linear parametric model can be estimated from a statespace structure. In general, the statespace model discretetime settings generally feature the following structure:where the represents the states of the system and represent the output, input and error.
The , , , matrices contain the model parameters, and is the sampling time of the system.
For modeling multiinput/multioutput (MIMO) systems, statespace models have been proven to be the most popular option, likely because the statespace method is relatively simple. For the system used in the present work (twelve inputs and twelve outputs), the discretetime statespace model for order 12 (sampled as Ts = 3600 s) and the A, C, and K matrices are as follows:
8. Conclusions
In this paper, the S. J. Carew building was modeled using the IDAICE program using all details of HVAC system and instructions of the building. This model provides good approximations comparing the consumption of hot water and electricity with the measured data for a full year (2016). It also compares the average of the outside temperature of the weather file of IDAICE program and the measured data. All system inputs and outputs were selected, and a linear statespace model was identified describing the thermal response of the system. The dynamic model is required to design and test system controllers before actual implementation. The model was derived using MATLAB’s System Identification (SI) Toolbox. The model has twelve state variables, twelve inputs, and twelve outputs. The model responses when compared with actual data are within the allowed range. Validation data and autocorrelation function for the residuals as well as the crosscorrelation function between input and residuals are computed and presented.
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
This work is funded by the Ministry of Higher Education of Libyan Government, which is managed by CBIE in Canada. Also, the authors thank the Honeywell Office and Department of Facilities Management at Memorial University for providing them with structure details of the building and access to Honeywell software of the HVAC system.
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Copyright
Copyright © 2019 Almahdi AbdoAllah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.