A Review of Fault Detection and Diagnostics Methods for Building Systems
Abstract
Faults in buildings systems touch on energy efficiency and occupancy comfort. Simulating edifice behavior and comparing it with measured data allows to discover discrepancies due to faults. Nosotros advise a methodology to recursively compare actual data with dynamic energy simulations at different layers of aggregation to reduce the scope in searching for faults through the development the Online Energy Simulator, a tool to gear up automated simulations using standard interfaces usable with unlike building systems and simulation engines. Nosotros test our simulator on a real building at the University of Southern Denmark, showing how continuous monitoring allows to quickly detect and identify buildings faults.
1 INTRODUCTION
Buildings are responsible for a large portion of energy consumption. In the The states they accounted for 7% of primary energy consumption in 2010, which is more than than transportation and industrial sector. Buildings energy consumption is also speedily increasing over fourth dimension, doubling from 1290 TWh in 1980 to 2784 TWh in 2010 [one]. In the European Marriage buildings business relationship for 40% of the full energy used and 36% of the total COtwo emissions [2]. Thus, the focus on buildings is central to accomplish the energy efficiency and environmental objectives, such equally the European goal of saving 20% of primary energy consumption by 2020 compared to projections [3], and thirty% by 2030 [four].
Modern buildings have circuitous control systems that monitor the electric current status and manage heating, cooling, ventilation and lighting. Each one of these subsystems has also increasing complexity, and can, therefore, endure from faults and malfunctions. Faults tin impact occupancy comfort, e.g. a cleaved radiator would result in a common cold room, merely tin besides yield higher energy consumption. It is estimated that in 2009 the most common faults in U.s. commercial buildings were responsible for over $3.3 billion in energy waste [5].
Without a continuous monitoring of the building, faults can happen and go undetected for a long time. Moreover, many fault detection methods rely on detecting changes from previous behavior, and are, therefore, ineffective in detecting faults present since the construction of the building. 'Energy models' of the buildings can be developed and used to assess that the actual free energy consumption follows the blueprint goals by simulating the building's behavior. 'Static' free energy models are simpler and require low computational ability but presume steady-country atmospheric condition and require simplifications. 'Dynamic' energy models are instead more than complex both to develop and to simulate but tin can accurately capture interactions betwixt components and changes over time.
In this commodity nosotros propose a methodology for fault detection and diagnostics (FDD) in buildings using free energy models simulations and comparing with existent building at different assemblage layers. Nosotros nowadays a software solution to automate simulations without relying on any manual procedure. Our tool uses industry standard interfaces to back up dissimilar simulation engines and automated information retrieval from the building. Nosotros then report the awarding and testing of our method and tool on a existent case study edifice.
Our tool was adult under the COORDICY Project, a strategic DK-US interdisciplinary research project for advancing ICT-driven research and innovation in energy efficiency of public and commercial buildings [six]. We use our tool to monitor the daily energy usage of our instance study building at several assemblage layers, such as whole building, by subsystem or by floor.
The rest of the article is organized as follows. The state-of-art is reviewed in Section 2. The FDD methodology is introduced in Section 3 and the Online Energy Simulator in Section four. Department five presents the instance study and discusses results and implications. Finally, conclusions are drawn in Section 6.
2 STATE-OF-Fine art
two.1 FDD in buildings
Kim et al. present a comprehensive review of FDD for building systems in recent years. FDD studies are classified using 2 different schemes: based on edifice equipment/size, such as large/pocket-sized buildings, Heating Ventilation and Air-Conditioning (HVAC) systems, lighting, water heaters and ventilation units, and based on method. FDD methods can be divided in history based and qualitative or quantitative model based [7].
History-based methods rely on the availability of historical information for a edifice. Such data is used to create black-box or gray-box models, often using machine learning techniques such equally artificial neural networks, for the system nether analysis. Faults affect the arrangement'southward behavior so that it does no longer lucifer the model'southward predictions. Historical-based models tin can be used when piffling or no data about the physical system under test is available and can in general stand for circuitous interactions. However, they require skillful quality fault-free grooming data and can only brand accurate predictions within its range. Moreover, they are specific to the system used for training and cannot easily be used on other ones.
Qualitative model-based methods rely on a priori knowledge of the system nether investigation. Such cognition, provided by documentation or expert knowledge, is used to create rule-based or qualitative physical systems. Qualitative model-based methods are unproblematic to implement and can unremarkably be validated past field experts. They are also usually robust to numerical doubtfulness in input data. Still, they oft upshot in rigid models that cannot be practical to different systems or easily extended.
Quantitative model-based methods rely on explicit mathematical models of system nether investigation. Such models, which accurately stand for the arrangement'due south physical function, are used to simulate the system'southward expected behavior, which can be compared with the actual one. Quantitative model-based methods provide the almost accurate results, and are usually able to simulate transients in dynamic systems, and fifty-fifty faulty behavior. However, such models are often complex and are both difficult to develop and computationally heavy. They also crave validation and parameter estimation with experimental data earlier their results can be trusted, and cannot easily be used with unlike systems.
Methods from each category have unlike trade-offs and are suitable for different kinds of systems. Hybrid approaches that brand use of multiple methods are likewise common, in order to exploit advantages and reduce disadvantages of individual methods. Using multiple methods also increases robustness and reliability.
2.2 Building simulation
Many simulation engines are available for simulating buildings energy performance, some explicitly oriented to this field, such every bit EnergyPlus [8], some more than generic, such as Modelica [9].
Clarke et al. depict the overall topic of building performance simulation, its aims and achievements both at the nowadays and in the future. The authors analyze the current state-of-fine art for building performance simulation tools with respect to different aspects, such every bit subsystems modeling, control, occupants representation, computation time and economic considerations [10].
Costa et al. discuss the advantages brought by monitoring buildings and comparing with energy operation simulations. The authors depict some of the available visualization techniques to brandish data obtained from building monitoring in a way to facilitate FDD. They also describe how results from monitoring can be used to improve model calibration and operations optimization [xi].
Maile et al. suggest a new methodology to compare results from simulations using free energy models to actual measured information. They consider the importance of multiple hierarchies, such as by component and past location, which tin can be used to better evaluate the results. An assessor should gather measurement and simulation assumption, perform simulation and collect information, and finally compare the results. All differences betwixt simulated and measured data must be categorized in either: measurements problems, simulation problems and operational bug. Non all differences are actually performance problems, some may be due to measurement or simulation assumptions. Models should be iteratively adapted to reflect the bodily building [12].
Wetter proposes a framework to connect several simulation engines together using Ptolemy 2 modeling environment as middleware to manage advice. The author defines an interface for communication between the engines and implements it for several engines such as EnergyPlus, Modelica, Matlab and Simulink. The author tests his framework by performing a co-simulation betwixt EnergyPlus and Modelica, exploiting the advantages of each engine in a particular domain [13].
Pang et al. present a framework for real-fourth dimension simulation synchronized with the bodily building using the simulation engine EnergyPlus. The simulation is managed using Ptolemy Two actors and a BACnet interface is used to commutation data with the Building Management System (BMS). The authors keep to test their methodology on a existent test bed and observe big differences betwixt measures and fake total energy consumption. However, when looking at disaggregate plots information technology is possible to figure out what are the causes. Difference of cooling energy consumptions has like peaks of departure of full energy consumptions, and they are caused by mismatch in chilling strategies between the model and the actual equipment. The aforementioned was noted in the case of lights left on overnight [14].
This framework supports only few selected simulation engines and only BMSs that publish information over a BACnet interface. In club to overcome these restrictions Pang et al. revise their work and re-implement their framework by using Functional Mock-upward Interface (FMI), which is a standard interface supported past many simulation engines. They too use the Simple Measurement and Actuation Profile (sMAP) to commutation data, which is an open protocol for information publication [15].
Sharmin et al. present a methodology for sensor-based monitoring of buildings and utilise it to two residential buildings and run data assay on the results. The authors bear witness how monitoring reveals not-obvious information and insights about free energy consumption, eastward.thou. heating loss was higher for units on middle floors, which suggests the need for meliorate insulation. The authors also observe that users react by improving their energy usage when introducing feedback from monitoring, only but curt term. Automated command is necessary to attain long-term results [16].
With most engines, users must perform repetitive, time-consuming and error-prone operations to setup and run a simulation. Outset they have to fetch the input data, optionally preprocess it, and convert information technology to the expected format (e.g. many engines expect information at stock-still intervals corresponding to the simulation stride). And then the model must be modified to signal to the right input data files. And then the user must manually start the simulation. Finally, the user can admission the results normally from a CSV file.
Often simulation results are interesting for multiple users. Either such users must each independently go through all the mentioned steps, or one user usually shares the results by 'unstructured' ways, such as sending files by email. The sometime option multiplies the necessary fourth dimension (and the potential for errors), while the latter presents other problems, such as misunderstandings with respect to successive versions of results and possibly authorization issues.
Finally, models in quantitative model-based methods are complex and strictly related to the equipment under test and, therefore, are difficult to generalize and apply them with unlike equipment. Different simulation engines are optimized for certain systems and users demand to learn the details of each of them. Thus, it appears evident that a solution able to automate simulations from different engines in a transparent way and make real-time results easily available online to multiple users is valuable.
3 METHODOLOGY FOR FDD IN BUILDINGS
Faults in buildings bear upon either occupants condolement or free energy consumption. We use a dynamic energy performance model to simulate the building's behavior and compute the expected energy consumption. Thus, any divergence of the actual energy consumption data compacted to the false results will highlight faults and anomalies to exist investigated.
Buildings tape energy consumption at different layers. There is a main meter for electricity that measures the entire edifice consumption and sub-meters for every organisation, such as HVAC and lighting. Some buildings besides have individual sub-meters for floors, other zones or other components. Split up energy distribution trees tin can be bachelor for hot water and commune heating systems, depending on the building. Figure i shows an instance of electrical energy distribution tree for a edifice. Sub-meters allow to split the aggregate data from the main meter and to understand how different systems utilise free energy in the building in a more clear and detailed manner. Building free energy models are able to provide results at different granularities, therefore, information technology is possible to compare actual and simulated values for sub-meters.
Figure 1.
Distribution tree in a building for electrical energy. The chief meter can exist decomposed in HVAC and its ventilation units, and in lighting, which can be in plow decomposed by floor, and miscellaneous.
Effigy 1.
Distribution tree in a building for electric energy. The primary meter can be decomposed in HVAC and its ventilation units, and in lighting, which can be in turn decomposed by floor, and miscellaneous.
In this study we develop and implement a top-down approach for FDD as shown in Figure 2: when a departure betwixt bodily and simulated values is detected at the main meter, the next sub-meters layer are compared to understand which system is affected past the mistake. This recursive investigation continues until reaching the leaves of the energy distribution tree. At this point the smallest unit or zone where the fault is located was identified. Later the scope was reduced, it is possible to use a more focused FDD method to completely isolate the fault.
Figure 2.
Acme-downward approach in fault detection and diagnostics. Comparing recursively different layers of the edifice'south distribution tree allows to reduce the scope of faults.
Figure ii.
Peak-down approach in error detection and diagnostics. Comparing recursively dissimilar layers of the building's distribution tree allows to reduce the scope of faults.
Let'due south assume, for example, that we accept detected a higher consumption of the building with respect to the district heating distribution tree. Hot water coming from the commune heating pipes is used to rut up air in the ventilation units and water in radiators. In our next footstep the imitation and actual values for the respective sub-meters are compared. If the radiators are establish responsible for the deviation, the ventilation units are and then excluded from the investigation and labeled as not faulty. Depending on the granularity of sub-meters, we could go deeper in the distribution tree and isolate the verbal areas responsible for higher energy consumption, and from at that place perform specific FDD for radiators.
iv ONLINE Energy SIMULATOR
The Online Energy Simulator is a tool that
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fetches required data for the simulation (due east.one thousand. weather atmospheric condition or occupancy count) from time serial on the data storage;
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maps such time series to a model's input variables;
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runs the simulation for a specified number of steps/period of fourth dimension;
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collects results from model's output variables; and
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posts results to the data storage.
All these operations are automatic and the Online Free energy Simulator can be run without any manual intervention. The high-level compages is shown in Figure iii.
Effigy iii.
Compages of Online Energy Simulator. All data are accessed through sMAP and the simulation engine is embedded in a FMU and operated through FMI.
Figure 3.
Architecture of Online Energy Simulator. All data are accessed through sMAP and the simulation engine is embedded in a FMU and operated through FMI.
The Online Energy Simulator uses the Simple Measurement and Actuation Profile (sMAP) for accessing building data, a protocol common for building systems [17]. The protocol supports reading and writing time series. Information technology also supports time serial metadata in class of key-value pairs. Metadata can be used to query the data storage for the correct time series. The protocol is contained of the underlying storage organization. In order to add back up for sMAP to a system information technology is plenty to develop a 'commuter', i.due east. an application that forward data from such organisation over sMAP.
In guild to back up unlike simulation engines, the Online Free energy Simulator uses the Functional Mock-up Interface (FMI). FMI is an interface to perform model exchange and co-simulation of dynamic models [18]. It allows to wrap an existing model in a self-contained Functional Mock-up Unit (FMU) and to make information technology available to other programs. A plan can run simulations through FMUs without any information most the actual simulation engine.
4.1 Configuration
FMUs expose input and output variable through the FMI. The Online Free energy Simulator uses a set up of configuration files to map such variables to time series. Input variables can be provided in three different ways.
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Explicitly: the variable's value is abiding over the whole simulation period and set in the configuration file.
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From a CSV file.
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From a time series on sMAP, identified past its Universally Unique IDentifier (UUID).
Bones arithmetic operations are also supported to allow unit of measurement conversion. For each input variable the Online Energy Simulator will either prepare a abiding time series, load it from the CSV file or fetch it from the information storage. Then it will laissez passer it to the FMU and start the simulation.
Output variables are mapped to sMAP time series past 'source proper noun', 'path' and 'UUID'. The Online Energy Simulator also supports setting metadata of output time serial, e.grand. its unit or its location. An case of mapping configuration is shown in Listing i.
Listing 1.
Example mapping configuration file.
Listing i.
Example mapping configuration file.
Besides input/output mappings the Online Energy Simulator reads from configuration files the path to FMU, simulation start/end time, simulation footstep size and sMAP connection details.
The FMU and configuration files completely define the behavior of the Online Energy Simulator. Therefore, it is simple to replace the model when a new more accurate version is available, or fifty-fifty to switch to a different simulation engine, as long as the new one supports the FMI.
4.1.1 Batch and real-time simulations
Necessary input data for the whole simulation period must be available at the beginning of simulation. This assumption holds for simulations over historical data, but not for simulations over present or time to come time, where information become available during the simulation itself. A naive solution would be to split the simulation period in single iterations and run contained simulations in sequence. For example, the Online Energy Simulator could simulate one twenty-four hour period at the time over a calendar week. Still, some engines such as EnergyPlus perform a certain amount of initial 'warm-upwardly' steps to compute initial values for room temperature and other measurements. This would result in discontinuities at the boundaries of each iteration.
To account for this use example, the Online Energy Simulator supports a special kind of execution. The simulation period is once more divided in single iterations, but the Online Energy Simulator stops at the terminate of each iteration and waits for user input. Then information technology fetches input data just for the 'next iteration period' (with the exception of weather data), and runs the next iteration. The warm-up stage is merely performed at the beginning of the first iteration, and all the measurements are continuous over the entire simulation period. User input for iteration start is deterministic and, therefore, the user tin exist replaced by some other plan.
four.2 EnergyPlus simulation engine
EnergyPlus is a whole building free energy simulation tool developed by the US National Renewable Energy Laboratory [8]. Information technology is used to simulate the building'southward beliefs and energy consumption over time, both at whole building level but likewise at room and subsystem level. It can simulate large diverseness of buildings subsystems such as HVAC, h2o and hot water distribution and lighting.
The model describing the building is contained in a single EnergyPlus Input File (IDF). This file contains information about the whole building envelope, such as walls, pavements and windows, their geometry, material and thermal properties, and about the edifice subsystems such equally ventilation units and lights. The building is divided in contained thermal zones that interact between each other over fourth dimension.
EnergyPlus supports wrapping its models to FMUs and to expose a motorcar-friendly interface usable past the Online Energy Simulator [nineteen].
4.2.one Weather file update
Due to using the FMI the Online Energy Simulator is engine-agnostic, i.due east. information technology supports EnergyPlus models but also models from other simulation engines, every bit long as they betrayal the correct interface. There is one exception, however, because EnergyPlus has limited back up for weather information equally input. Instead, weather data must be provided in the class of an EnergyPlus Atmospheric condition (EPW) file, and it needs to exist available at FMU 'cosmos fourth dimension'.
Since providing updated weather condition information at execution time is a useful utilize case, the Online Energy Simulator supports this EnergyPlus-specific feature. FMUs are in practice renamed Zilch files containing the simulation engine (or a wrapper to call the actual engine) in form of a shared library. FMUs created from EnergyPlus contain likewise additional files, i.e. the model IDF file and an EPW file.
When the Online Energy Simulator loads an FMU it decompresses its Nothing file, replaces the interesting columns of its EPW file with weather data provided as input and re-compresses equally a new Zippo file. In this way it is possible to provide weather data at the showtime of a simulation. Providing conditions data equally input 'during the simulation', such as for occupancy data or setpoints, is non possible due to limitations of EnergyPlus engine.
v CASE Written report: BUILDING OU44
In this commodity we nowadays Odense Undervisning Building 44 as case report [twenty]. The building, shown in Figure 4, is located at University of Southern Denmark, campus Odense and was built in 2015. Information technology has 4 floors and is mainly used for instruction and information technology consists of classrooms, study rooms and offices. Regarding the HVAC system, there are four ventilation units, each serving i of the corners of the edifice. In addition, the building is heated using a commune heating loop and, partially, through the ventilation system.
Figure iv.
Odense undervisning edifice 44 at University of Southern Denmark, campus Odense.
Figure 4.
Odense undervisning building 44 at Academy of Southern Denmark, campus Odense.
Every room has the following sensors:
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Temperature [celsius];
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CO [ppm];
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PIR [boolean]; and
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Light [lux].
Some rooms take boosted sensors or meters. For case some have separate meters for plug load or sensors for humidity. Four examination rooms are equipped with occupancy counting cameras that provide an estimate of people in the room. In addition to that, the building has a weather station that records outdoor temperature, wind speed, rain and solar radiation. At that place are also several energy meters: for heating, ventilation, hot water, lighting, plug load, usually aggregate by floor or area. Finally, occupancy counting cameras are also located at every entrance of the building, providing an estimate of people in the entire building.
All sensors are accessible through a KNX double-decker [21] and broadcast records to the BMS according to their configuration. All energy meters are accessible through an EnergyKey organization. Custom drivers fetch data from the BMS and EnergyKey system and publish it to a centralized information storage using sMAP, so that information technology is available to other applications, such as occupancy prediction [22] and model development and calibration [23].
5.1 Monitoring building performance with Online Energy Simulator
An overall dynamic energy performance model for the OU44 model was developed past Jradi et al. [20] considering diverse edifice characteristics and specifications including concrete envelope, free energy supply systems and operational parameters. The building model is continuously re-calibrated within the developed framework, considering a 3 months timeframe. The model was prepared for export past exposing selected input/output variables in the interface. This step is automatic using the EPQuery tool [24], which helps to modify EnergyPlus IDF files using Python scripts. Employing the developed dynamic model, the Online Energy Simulator was configured and deployed to the case study building Odense Undervisning Building 44 (OU44) to monitor its energy performance. Once per day a simulation is run over the previous 24 h providing the following input data:
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Weather information from the local weather station: outdoor temperature, wind speed and solar radiation.
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Whole building occupancy information, obtained from occupancy counting cameras.
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Single room occupancy data for the four test rooms that have occupancy counting cameras.
We focused on the four test rooms because having an estimate of the occupants count helps understanding their dynamics. These rooms also take additional room level energy meters and higher resolution sensors.
The following output variables were nerveless at each simulation step, i.e. 10 min, and posted to data storage:
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Whole building electric energy consumption.
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Whole building heating energy consumption.
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Whole edifice lighting energy consumption.
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Electricity consumption for the four ventilation units.
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Room temperature for the 4 examination rooms.
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CO level for the iv test rooms.
An overall building occupancy profile was generated using input from the different camera counts around the building [25]. The model assumes that occupants spread uniformly over the entire building. For the four test rooms, however, specific occupancy count estimates are provided to improve simulation accuracy.
One time results are posted to data storage, they are available to every other application. In detail, simulation results can exist compared with the actual measured values. This allows to observe whatever deviation or differences between the actual and predicted functioning of the building.
5.ii Results
In this department we bear witness the results obtained by running the Online Energy Simulator on OU44. We used an EnergyPlus model and we ran simulations for 8 months from Thursday 1 September 2016 to Lord's day fourteen May 2017. We provided whole building occupancy count, room level occupancy counts for four test rooms, outdoor temperature, wind speed and solar radiation every bit simulation input. We testify charts for selected time periods.
v.ii.ane Results for energy operation
Effigy 5 shows the simulated and measured electrical energy consumption over a week for building OU44. Cumulative energy consumption over fourth dimension is shown on the left column and energy consumed every 2 h is shown on the correct column. Nosotros chose this value because some of the sub-meters accept low fourth dimension resolution, which resulted in spikes using shorter values. The last row shows the total occupants in the edifice, estimated through the occupancy counting cameras.
Figure v.
Data from energy meters and simulation results for building OU44. Cumulative free energy consumption over time is shown on the left column, energy consumed every 2 h on the right 1. Total occupants in the buildings are shown on the last row.
Figure 5.
Data from energy meters and simulation results for edifice OU44. Cumulative energy consumption over fourth dimension is shown on the left column, free energy consumed every ii h on the right one. Total occupants in the buildings are shown on the last row.
Free energy performance at the whole edifice level is on par with the simulation results, with a small deviation toward the end of the calendar week. We consider the adjacent sub-meters layer, i.e. the ventilation arrangement and lighting. The remainder of energy consumption is due to building operations, such as elevators and plugs load. We observe two singled-out phenomena: the ventilation system performs consistently worse than the model, and energy consumption for lighting deviates significantly during the weekend.
We can explain the bibelot for lighting by looking at occupancy over time. During the weekend, occupants count drops but the edifice is not completely empty. It is possible that a small number of students come to study on weekends and spread to different rooms. In this example the lights would be turned on for many rooms even with a modest number of occupants, while the model assumes a proportional lighting energy consumption.
We proceed our investigation of the ventilation system and examine the sub-meters in the next layer, i.e. at the individual ventilation units. Unit 1 follows closely the simulation, only the other three deviate. Units 2 and 4 consume less energy than expected, while unit iii consumes significantly more. There are no more meters in the ventilation units, therefore, we cannot further compare false and measured functioning. We succeeded in reducing the scope to ventilation unit iii, which has a large deviation from the expected performance and now nosotros can run specific FDD techniques to completely isolate the faulty component. Further investigation should also be performed to empathize why ventilation units 2 and iv have a lower energy consumption than expected.
v.2.2 Results for indoor conditions
In add-on to free energy meters, nosotros compared the room level indoor atmospheric condition measured by building sensors with the ones from the simulation.
Figure 6 shows the imitation and measured room temperature for 1 of the four test rooms. Although the dynamic EnergyPlus model was calibrated based on the overall free energy consumption of the building, actual room indoor air temperature were found to be in line with the model predictions, with the ii values following the aforementioned trend. Nonetheless, it is noticed that room temperature measured by the edifice sensor quickly drops during the night of Tuesday 4 Apr 2017, deviating from the false value.
Figure half-dozen.
Comparing between simulated and measured room temperature. Temperature dropped sharply during i dark, post-obit the outdoor temperature.
Figure 6.
Comparing between simulated and measured room temperature. Temperature dropped sharply during i night, following the outdoor temperature.
We can explain this anomaly past noticing that the indoor temperature follows closely the outdoor temperature recorded past the edifice's weather station. The most likely cause was that the room windows were left open up during the dark.
5.ii.3 Computational load of simulations
In guild to gauge the computational load of simulations we ran the Online Free energy Simulator over periods of different lengths and recorded the elapsed fourth dimension. The results are shown in Table I. Simulating an entire mean solar day or even an entire month only takes few minutes. The elapsed times are very similar fifty-fifty for very unlike simulation periods because EnergyPlus spends long time during the warm-upward phase, which is the same for every simulation.
Table 1.
Elapsed time for different simulation periods.
| Simulation period | Elapsed fourth dimension |
|---|---|
| 1 d | 279 s |
| 7 d | 349 due south |
| 30 d | 518 s |
| 60 d | 683 s |
| Simulation catamenia | Elapsed time |
|---|---|
| 1 d | 279 south |
| vii d | 349 southward |
| xxx d | 518 s |
| threescore d | 683 s |
Table 1.
Elapsed time for dissimilar simulation periods.
| Simulation menses | Elapsed time |
|---|---|
| 1 d | 279 s |
| vii d | 349 south |
| thirty d | 518 s |
| 60 d | 683 s |
| Simulation menstruation | Elapsed time |
|---|---|
| 1 d | 279 s |
| vii d | 349 due south |
| 30 d | 518 s |
| sixty d | 683 s |
6 CONCLUSIONS
We proposed a method for FDD in building systems using dynamic energy models to simulate the expected behavior of the edifice and compare it with the actual one at unlike layers. We presented a tool for scheduling and automatically running simulations without user interaction, using manufacture standard interfaces to support many simulation engines and building systems. Finally, we tested our method and tool on a real building, identifying anomalies in energy consumption of lighting and ventilation units, and in room temperature. Equally the tool was implemented for a short time for validation in the instance report building, the savings due to the implementation were not evaluated, but major expected savings include less operational costs, higher maintenance process response, lower energy consumption and higher thermal comfort.
Splitting energy consumption in sub-meters immune us to sympathize how different subsystems use energy within our building. Nosotros were able to follow the energy distribution tree from its root to its leaves, ruling out branches where measured values were on par with simulation results and exploring the ones where the they deviate. We succeeded in identifying the ventilation unit responsible for college energy consumption and gained insights nearly the lighting system.
We as well showed how using an automated solution to schedule simulations tin can reduce the risk for human errors. The Online Energy Simulator developed and presented in this study has been running automatically for several months in the OU44 building within the 'ObepME Tool', Online Building Free energy Performance Monitoring and Evaluation, for automatic and continuous energy monitoring and evaluation of the overall edifice energy performance aiming to reduce free energy operation gaps and forming a backbone for FDD [26]. Thanks to a configuration-based approach, we are able to hands upgrade and calibrate the dynamic model to newer versions and repeat simulations over whatsoever period with whatever functional changes.
vi.one Future work
The methodology proposed in this article covers the high-level identification of a faulty subsystem, and represents an important intermediate block of a complete FDD solution for building systems. In club to perform a full FDD it is first necessary to ensure validation of input information—which nosotros previously approached in [27]—and then to use specific methods to completely isolate the faulty component inside the identified subsystem. Those methods should exploit the characteristics of the considered systems, such every bit individual ventilation units or room lighting, to reach the best FDD operation. Moreover, faux and measured data are both available on our data storage for client applications, but they are not accessible in a user-friendly way. A dashboard application would enable non-technical users to appraise the building status and performance.
Furthermore, we are extending the Online Free energy Simulator to play an important role as component of a new 'virtual edifice'. The virtual building behaves as closely as possible to a real edifice, also with respect to control input. It waits for new actuation commands to be posted to our data storage and simulate the outcome. A BMS tin can then be deployed on the virtual edifice making possible to examination our control strategies before deploying it on a real one.
ACKNOWLEDGEMENT
This work is supported by the Innovation Fund Kingdom of denmark for the project COORDICY.
REFERENCES
1
U.S. Department of Energy
. Buildings Free energy Data Book. Tech. rep. U.S. Department of Energy,
2011
.
2
European Parliament, Council of the European Union
.
Directive 2010/31/European union of the European Parliament and of the Council on the energy performance of buildings
.
Off J Eur Wedlock
2010
;
L153
:
13
–
35
.
3
European Parliament, Quango of the European Matrimony
.
Directive 2012/27/Eu of the European Parliament and of the Council on energy efficiency
.
Off J Eur Marriage
2012
;
L315
:
i
–
56
.
4
European Committee
. Proposal for a Directive of the European Parliament and of the Council amending Directive 2012/27/European union on energy efficiency.
2016
.
5
Mills
Eastward
.
Edifice commissioning: a golden opportunity for reducing energy costs and greenhouse gas emissions in the U.s.a.
.
Energy Efficiency
2011
;
4
:
145
–
73
.
.
7
Kim
W
, Katipamula South
A review of fault detection and diagnostics methods for building systems
.
Sci Technol Built Environ
2018
;
24
:
3
–
21
.
.
8
Crawley
DB
, Lawrie LK Pedersen CO
1999
:
81
–
eight
.
nine
Fritzson
P
.
Introduction to Modeling and Simulation of Technical and Physical Systems with Modelica
.
John Wiley & Sons, Inc
,
2011
.
.
10
Clarke
J
, Hensen J
Integrated edifice performance simulation: progress, prospects and requirements
.
Build Environ
2015
;
91
:
294
–
306
.
.
11
Costa
A
, Keane MM Torrens JI
Building operation and free energy performance: monitoring, assay and optimisation toolkit
.
Appl Energy
2013
;
101
:
310
–
16
.
.
12
Maile
T
, Bazjanac V Fischer Yard
A method to compare simulated and measured data to assess building energy performance
.
Build Environ
2012
;
56
:
241
–
51
.
.
13
Wetter
G
.
Co-simulation of edifice free energy and control systems with the Building Controls Virtual Test Bed
.
J Build Performance Simul
2011
;
4
:
185
–
203
.
eprint: 10.1080/19401493.2010.518631.
14
Pang
X
, Wetter Thousand Bhattacharya P
A framework for simulation-based existent-time whole edifice performance cess
.
Build Environ
2012
;
54
:
100
–
8
.
.
15
Pang
Ten
, Nouidui TS Wetter M
Building energy simulation in real time through an open up standard interface
.
Energy Build
2016
;
117
:
282
–
9
.
.
16
Sharmin
T
, Gül Yard Li X
Monitoring building free energy consumption, thermal performance, and indoor air quality in a cold climate region
.
Sustain Cities Soc
2014
;
xiii
:
57
–
68
.
.
17
Dawson-Haggerty
S
, Jiang X Tolle G
2010
:
197
–
210
.
.
18
Schwan
T
, Unger R Pipiorke J
2017
:
73
–
8
.
.
20
Jradi
Chiliad
, Sangogboye FC Mattera CG
22
Lazarova-Molnar
South
, Baun Kjærgaard M Shaker H
2015
:
306
–
12
.
.
23
Jradi
Grand
, Veje C Jørgensen BN
2016
.
25
Sangoboye
FC
, Kjærgaard MB
2016
:
147
–
56
.
.
26
Jradi
M
, Arendt K Sangogboye FC
ObepME: an online edifice energy performance monitoring and evaluation tool to reduce energy performance gaps
.
Energy Build
2018
;
166
:
196
–
209
.
.
27
Mattera
CG
, Lazarova-Molnar Southward Shaker HR
2017
:287–292. DOI:
.
© The Author(s) 2018. Published by Oxford Academy Press.
This is an Open up Access article distributed nether the terms of the Artistic Commons Attribution License (http://creativecommons.org/licenses/by/iv.0/), which permits unrestricted reuse, distribution, and reproduction in whatever medium, provided the original work is properly cited.
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