DATA ANALYSIS IN THE INTELLIGENT BUILDING ENVIRONMENT

International Journal of Computer Science and Applications © Technomathematics Research Foundation Vol. 11 No. 1, pp. 1 - 17, 2014

DATA ANALYSIS IN THE INTELLIGENT BUILDING ENVIRONMENT

DALIA KRIKSCIUNIENE

Faculty of Informatics, Masaryk University, Brno, Czech Republic, [email protected]

TOMAS PITNER

Faculty of Informatics, Masaryk University, Brno, Czech Republic,

[email protected]

ADAM KUCERA

Faculty of Informatics, Masaryk University, Brno, Czech Republic, [email protected]

VIRGILIJUS SAKALAUSKAS

Department of Informatics, Vilnius University, Vilnius, Lithuania,

[email protected]

The article addresses the problem of intelligent analysis and evaluation of facility management data gathered from heterogeneous sources, including environmental data collected from building automation sensors, temporal weather characteristics and scheduling. We suggest the framework of analytical model, based on deriving descriptors which could sentinel the level of thermal comfort of working environments. The model aims to facilitate process of extracting essential characteristics of facility management for detecting dependencies and observing anomalies. The framework aims to discover hidden relations between performance of building conditioning and environmental and spatial factors that cannot be observed from the building automation system itself. The performance of the model was tested by experimental analysis of facility management of the university campus, designed for exploring how various environment variables affect temperature in the lecture rooms, equipped by the air conditioning devices. Based on the obtained results, we elaborate on further steps needed for beneficial, efficient and flexible data analysis system for the field of facility management.

Keywords: Facility management; computational intelligence; machine learning; sensor networks; system integration; environmental conditions.

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2 Kriksciuniene, Pitner, Kucera, Sakalauskas

1. Introduction

Modern buildings tend to be equipped by various technologies, thus raising theproblem of facility management. IFMA ( International Facility Management As-sociation) defines facility management as ”a profession that encompasses multipledisciplines to ensure functionality of the built environment by integrating people,place, process and technology” [IFMA (2014)]. The complexity of the high-leveldecision-making in this area emanates from lack of knowledge and undefined causalrelationships between data flows coming from multiple sources including low-levelsensor networks, geographical data systems (GIS), weather conditions and variousother inputs taken into account by facility managers according to their experienceand expertise. The concept of “intelligent buildings” is becoming standard for moni-toring building sites, where the prevailing approach is based on cost and investmentanalysis [Wong, Li and Wang (2005)].

This domain area can also be analyzed as interplay of related subsystems whereeach of them follows its own goal, such as maximizing perceived comfort by theusers, increasing quality and effectiveness of facility control procedures, providingsupport and advice for facility management personnel, at the same time minimizingenergy consumptions and maintenance costs.

The problem which emanates from the perspective of system interplay can beformulated as search for methods which can resolve the gap between indicatorsplaced in different time scales: the cost and energy consumption indicators used forcontrol are “lagging”, as they reflect past performance. The data provided by sensorsand other environmental characteristics can be called “leading”, as they containhidden information advancing future value of the “lagging” indicators. The problemis also affected by the “intermediate” variables describing comfort of the users whichhave individual tolerance ranges to changing conditions of the building environment.In many cases the facility managers are not supplied by the information of the“lagging” variables, as their decisions have to be based on real-time data flow.

In this article we focus on intelligent analysis of building data by searching causalinterrelationships between “leading” variables which could lead to deviations andanomalous states of facility management system. Sections 1 and 2 describes domainof facility management, presents the overview of state-of-the-art in the use of com-putational intelligence and machine learning methods in the fields of automationand regulation of intelligent building operation based on monitoring sensor net-work. Section 3 proposes the method for building management data analysis anddescribes use case we performed computations on. Section 4 evaluates result of theproposed method. Section 5 elaborates on further steps toward complex tools forintelligent facility management data analysis. The last section concludes the resultsand outlines topics for further research.

Data Analysis in The Intelligent Building Environment 3

2. Intelligent Facility Management Systems

Construction and operation of modern building or multi-building site is the inter-disciplinary research area, where various scientific fields and application domainsare interrelated (Fig. 1).

Fig. 1. Operation of modern facility management.

All the aspects of building operations are closely connected for buildingenvironment-friendly and cost effective buildings, able to provide appropriate en-vironment conditions for their inhabitants as well [Prez-Lombard, Ortiz and Pout(2008)].

This task is achieved by improving control loops in regulation and automationwith the help of various techniques such as machine learning or modeling appliedfor various types of data sets. Operation of the building system is monitored andcontrolled by using data provided by sensor networks, operator workstations, webapplications, tenant portals, and archive databases. The complexity levels of anal-ysis include data mining algorithms, complex processes and event analysis. Facilitymanagers need tools which simplify management and maintenance by integratingdata from various sources, allowing remote control of different technologies fromone access point. In large organizations these systems may include services suchas helpdesk, preventive maintenance and inspection evidence, room reservations,space planning or tools for real-time controlling and monitoring of various buildingsystems [Atthajariyakul and Leephakpreeda (2004)].

Various standard systems for security, maintenance or access control of building,such as CCTV (closed circuit television), lighting control, elevator monitoring, andHVAC (heating, ventilating, air conditioning) are integrated into complex BMS


(Building Management System) system for monitoring and control of all subsystemsinstalled in the particular building (Fig. 2).

Fig. 2. Building management system, BMS.

The BMS provides bus (usually computer network) designed for mergingdata from various systems by using common communication protocol (BACnet,KNX/EIB, LON) [Merz, Hansemann and Hbner (2009)]. The architecture of BMSconsists of several operator workstations, web server, archive server connected tothis bus, which allows monitoring and controlling current state of systems in thebuilding.

The research literature focusses on analysis, optimization, device schedulingbased on the historical data provided by various sensors (temperature, humid-ity, energy consumption sensors) and performing computations based on historicaltrends observed in the data time series. A number of intelligent systems can be usedfor these tasks [Doukas, et al. (2007)], such as application of artificial neural net-works [Atthajariyakul and Leephakpreeda (2008)], [Yang and Kim (2004)], [Moonand Kim (2010)], [Mohanraj, Jayaraj and Muraleedharan (2012)], decision support[Shen, Hao and Xue (2012)], expert systems [Orosa (2011)] fuzzy logic control sys-tems [Dounis and Manolakis (2001)], [Kristl, et al. (2008)], simulation, clusteringor outlier detection [Seem (2007)], [Seem (2005)]. The selection of methods highlydepends on data sources.

The most common indicators for building operation efficiency evaluation arebased on the “lagging” indicators, such as overall building electric energy consump-tion. These data can be obtained by connecting main electricity consumption meterto the Building Automation System (BAS) with the help of specialized protocols


such as M-BUS or MODBUS; or higher level protocol (such as BACnet) [Seem(2007)], [Seem (2005)], [Xiaoli, Bowers and Schnier (2010)]. The delay of providingconsumption information creates main drawback of existing analytical models andis often solved by using simulated data.

Electricity consumption data can be obtained not only by providing absolutevalues, but also as daily increments, which serve as useful indicator for comparativeconsumption analysis of buildings with similar technical equipment.

Another type of metrics of HVAC is user centered. They include number of usercomplaints related to the ambient conditions of the building or Predicted mean vote(PMV) index. This index estimates number of discomforted users and integratesvarious parameters such as clothing, characteristic of human metabolism and variousenvironment variables ( temperature, humidity). The PMV index is suitable asoptimizing parameter for HVAC system, or regulation of the heating units [Cigler,et al. (2012a)], [Cigler, et al. (2012b)].

As the user centered approach explores ranges of variables defining comfort ofenvironmental conditions, we can assume that avoiding outliers and anomalous val-ues can lead to optimal values of the energy consumption and costs. The anomalousprocesses often mark fault or wrong setup of the building automation or inappro-priate use of the building equipment [Seem (2007)], [Seem (2005)], [Xiaoli, Bowersand Schnier (2010)].

In this article we focus on analysis for securing thermal comfort of building en-vironment reflected by sensor data of HVAC systems. As HVAC operation expensesmake significant share of overall building operation cost, therefore efforts to makeHVAC system more efficient are highly feasible.

3. Intelligent HVAC Analysis Method

We suggest method for evaluating impact of factors which could influence room en-vironment and in particular the stability of air temperature. We utilize intelligentanalysis methods allowing facility managers to compare combined effect of avail-able factors which tend to significantly vary over time, and to decide which of themshould be taken into account during building operation optimization. For experi-mental analysis we focus on the use of easily accessible data of room temperatures,supplied from the archive server and joining data from several other sources, in orderto extract easily understandable characteristics of complex processes which affectHVAC operation. The method was previously presented at IIMSS 2013 conferenceand published in [Kriksciuniene, et al. (2013)].

The novel feature of the model is based on intelligent analysis of building databy exploiting causal interrelationships between the derived indicators and arrangedin the four levels based on their cause-effect relationships, in order to resolve thegap between indicators placed in different time scales: “lagging” and “leading”. Theindicators of cost and energy consumption are desired to be used for reflecting pastperformance and control. However these indicators are not available at the moment


of decisions of facility managers. Therefore the real- time data provided by sensorsand other environmental characteristics can be called “leading”, as they containhidden information capable to forecast future value of the “lagging” indicators ofresource consumption and serve as basis for decision making.

The analytical model consists of two compounds:

(1) Preparing data flow for analysis. It consists of deriving “leading” indicators byextracting and aggregating data from internal and external systems potentiallyhaving the causal relationships and capacity of influencing “lagging” indicators.In Fig. 3 the suggested levels of indicators are presented: measures included tothe level of “Resource indicators” represent the “lagging” indicators which areinfluenced by the “leading” indicators presented in the levels of “User centeredmetrics”, “Building parameters” based on construction qualities and also bythe effects of “Weather conditions”. The values of the selected indicators canbe distorted or affected by the behavior of users of the facilities. In order toreveal these effects the level of “Dwelling processes and habits” is formed ofindicators describing choices made by users and influence of number of peoplepresent in the facilities.

(2) Applying computational intelligence methods for defining causal relationshipsbetween levels of indicators and also for defining most influential indicators foranalysis. In the presented analysis we applied clustering, regression, sensitivityanalysis, which can further lead to application of hierarchical fuzzy and anomalydetection methods.

3.1. Context

The experimental use case for illustrating the depth of the problem domain is se-lected on the basis of Building Management System (BMS) used for controlling andmonitoring operation of Masaryk University campus at Brno, Czech Republic. Theconstruction of the campus started in 2007. The concept of connecting all the facil-ity management data by capturing systems of over 25 buildings into the BMS wasimplemented by using BACnet protocol. The network consists of two web servers,two archive servers, operator workstations, and over 700 devices. These devicesfunction as PLCs ( programmable logic controllers) for controlling mainly HVACsystems and application gateways for the connected systems of access control, se-curity system and fire safety system. This enormous data flow is interconnectedwith the data from other systems of Masaryk University, such as GIS database ofbuilding passport, or Academic information system. The detailed data flow buildsbasis for designing advanced analysis of BMS.

3.2. Setup

The experimental data is composed of inputs from 15 lecture rooms which belongto one of the buildings of University campus. Each room is equipped with local (in-


Fig. 3. Levels of indicators included to analytical model.

room) air-conditioning (AC) units, which can be controlled by control panel locatedinside the room. Users are enabled to switch AC unit on or off and change desiredroom temperature. AC unit controls the speed of fan which supplies cold air intothe room, opens or closes valves of central heating radiators. AC unit automaticallyswitches off if windows in the room are opened. The examined rooms are locatedover all three floors of the building; they also differ in size, area, geographical orien-tation and number of the windows. These data are stored in spatial database calledBuilding passport of Masaryk University, which contains geographical data of allthe buildings of the university.

3.3. Measurement

The total size of dataset is 61920 records. It involves 4128 records per each of 15lecture rooms. We assume that the thermal regime is optimal if the room tempera-ture has high stability over time series. For the preliminary exploring of the use casewe choose the output characteristics of standard deviation of inside temperature inrelationship to 22 ◦C as prevailing preset temperature of the conditioning of lecturerooms. The investigation is applied for the data registered between 6:00 and 20:00.In this period room environment is infuenced by number of factors that are notpresent in the night (number of occupants, amount of sunlight).

The time period of analysis is from 1st of April to 13th of May suitable to


observe how weather conditions can affect environment inside the buildings withoutinfluence of heating system. Data about actual and desired room temperature wereobtained by using 15 minutes polling. Weather conditions (outside temperature,pressure, humidity, downfalls) were obtained from the on-line free meteorologicalinformation source [InMeteo (2012)] for Brno location with 30 minutes polling. Wejoined the time series of room temperature from BMS together adequately withdata from the meteorological station.

3.4. Processing

After processing data by applying intelligent analysis module of Statsoft tool [Stat-Soft Inc. (2011)] the seven aggregated data variables are presented in Table 1: stan-dard deviation temperature (Stn dev), inside temperature mean (T I), height (H),number of windows (W), room area (A, m2), geographical orientation O (East(E),West(W), inside (I) of building), and maximum capacity for people in room (C).

Table 1. Aggregated variable data.

Room No. Stn dev T I H W A O CBHA12N03034 0.90 22.00 2.80 2 164 W 259BHA12N02006 0.91 22.43 2.80 13 116 E 237BHA12N01032 1.04 22.20 4.15 0 217 W 126BHA12N02035 1.06 22.58 2.80 2 58 W 114BHA12N02036 1.08 22.66 2.80 2 56 I 40BHA12N02005 1.13 22.73 2.80 13 126 I 40BHA12N02034 1.24 22.50 2.80 13 126 E 32BHA12N03027 1.31 22.97 2.80 12 84 E 50BHA12N03005 1.39 22.61 2.80 13 126 E 50BHA12N03011 1.51 23.17 2.80 12 84 W 126BHA12N01014 1.59 21.33 4.15 0 217 W 114BHA12N03033 1.92 23.56 2.80 7 79 I 40BHA12N02011 2.15 24.01 2.80 12 67 I 40BHA12N03006 2.67 24.32 2.80 13 114 E 32BHA12N02028 2.77 24.60 2.80 12 68 E 130

The first step of analysis aimed to group rooms by their characteristics. InTable 1 the lecture rooms are ranked according to the value of standard deviation.Three groups can be distinguished: smallest values of Stn dev are registered for thefour rooms with large capacity and area, the medium Stn dev group contains fiverooms with small capacity and the last group with the largest values of Std. is mixedin all characteristics, which may imply differences in technical equipment installed


or other root causes not reflected by the collected data. The average temperatureT I tends to increase together with the increasing value of Stn dev, it suggestslow cooling capacity, higher influence of outer weather characteristics or ineffectivefacility management.

At the second step, the values of standard deviation are explored in relationshipto the room characteristics and the weather conditions. In Table 1 we observe thestrongest positive relationship of Stn dev to height and number of windows; thevalues of Stn dev tend to decrease if the area of room increases. The three char-acteristics of outer weather conditions do not affect Stn dev according to values ofp-level in Table 2.

Table 2. Summary of multiple regression for full data set.

From Table 2 we assume that the importance of the variables changes in all threegroups of rooms (Table 1). As the 3rd group consists of rooms with almost identicalcharacteristics as in group 1, we can assume that the main influence lies outside thesupplied data for analysis variables. E.g. the rooms BHA12N03011, BHA12N01014and BHA12N02028 have high capacity but the conditioning quality is low.

In the following steps we analyzed the influence of variables for each separategroup of rooms. In each group the weather conditions (outer temperature, humid-ity, and pressure) had no influence for the Stn dev values. In Table 3 the resultsare combined for the two groups of medium and low values of standard deviation(Stn dev). Here the construction-based characteristics of rooms have even biggerinfluence, as is expressed by the increase of p-level.

At the third step, the data set was explored by cluster analysis in order toget insight of distribution of values of the variables affecting stability of thermalcomfort. The Viscovery Vsomine tool [Viscovery Vsomine (2008)] was applied fordata analysis. The best separation with different interrelated effect of the variableswas achieved by making four clusters.

In Fig. 4 and Table 4 can observe big differences in ranges of the variable valuesand the consequent values of the Stn dev (both negative and positive). Each clusterhas its distinguished characteristic which can be assumed as a separation basis


Table 3. Results of subset of medium and high values Standard deviation (Stn dev).

Fig. 4. Variable value distribution of among clusters.

(large height and absence of windows for C4, lowest value of Stn dev for C3).


Table 4. Clusters characteristics.

3.5. Interpretation of results

The experimental research showed that the enormous technical data flow does notprovide sufficiently meaningful information for decision making in facility manage-ment. The root causes lay in the influences which are not properly captured bysensor data or intuitively overestimated (as weather conditions in our research).The further research of revealing important “leading” variables is planned in thedirection of measuring influence of time of people presence in rooms, space volumeper person, etc. and also refinement of the characteristics related to registering op-erating periods of the conditioning devices and their preset conditions. The designof workflow for clustering analysis showed that cluster separation implies combinedinfluence of varying ranges for the variables, which suggests further use of fuzzymethods for investigation.

4. Model Evaluation

We use the computational approach of analysis, which assumes that the analyticalmethods have to explore combined influence of numerous factors from inside andoutside environments in order to reveal their influence and sensitivity of decisionmaking by facility managers.

The novel feature of the model is based on intelligent analysis of facility man-agement data by exploiting causal interrelationships between the derived indicatorswhich are designed from the sensor based facility management system and arrangedin four different type of levels based on their cause-effect relationships, in order toresolve the gap between indicators placed in different time scales: “lagging” and“leading”.

The experimental research is based on analysis of sensor network data flow ofuniversity campus. The research showed that the indicator of stability of thermalcomfort has different dependence on the analyzed variables. The outer variables ofweather temperature, humidity and pressure had lowest influence, the room height,number of windows and square area had different importance in separate clustershaving low, medium and high values of thermal deviation. However application oftemperature sensors for facility management decision making was not sufficient,therefore, increasing number of variables and exploring their importance for thethermal comfort can increase precision of analysis.


5. Towards Intelligent Facility Management

The problem of the research in the area of facility management is highly influencedby the interdisciplinary characteristics: it depends on construction technologies, in-telligence and quality of control devices, and approaches for analysis. The problemwhich emanates from the perspective of system interplay can be formulated as searchfor methods which can resolve the gap between indicators placed in different timescales: “lagging” indicators of cost and energy consumption are used for reflectingpast performance and control. The data provided by sensors and other environ-mental characteristics can be called “leading”, as they contain hidden informationadvancing future value of the “lagging” indicators. As decisions of facility managershave to be based on real-time data flow, our research setting was designed to relyonly on “leading” characteristics. In this article we focus on intelligent analysis ofbuilding data by searching causal interrelationships between variables which couldreveal deviations and anomalous states of facility management systems.

5.1. Current state

Data analysis based on “lagging” indicators is sufficiently supported in CAFM(Computer-Aided Facility Management) software systems, but such tools don’t pro-vide wide support for analysis of building operation data. Both CAFM productsand building management systems are supposed to help the facility managers toreduce building operational cost. Interconnection and cooperation of those separatesystems is a promising way to optimize fault detection and recovery and shortenthe delay between acquiring the data by the BMS and performing actions leadingto optimization of building operation. One of the possible approaches to integrationof various information systems used by facility managers is described in [Shen, Haoand Xue (2012)]. Next, we describe proposed integration methods for BMS basedon BACnet protocol and Archibus CAFM software in the environment of the UKB.We will discuss two sample integration use cases - simplifying maintenance workflowand energy consumption analysis.

5.2. Building maintenance

On-Demand maintenance support in the CAFM software relies on system of re-quests. A request is usually entered into the system by maintenance staff or facilityusers. Then, it passes different phases (assigning, scheduling, material and tool re-questing, billing, checking) of its life cycle which ends when the issue is fixed. Thisprocess of fault resolution could be simplified by automated approach to creatingrequests in the CAFM software whenever fault is detected. For example, rooms withunsatisfactory temperature comfort detected by tool presented above could producerequest for maintenance of AC units in affected rooms.

The main problem of this approach is caused by insufficient data validity. Forthe on demand maintenance system to be effective, the CAFM software must not


be polluted by false alarms, trivial, or temporary problems. Complex algorithmsimplemented in advanced analytical tools can help with the task of filtering validissues that should be transferred into the CAFM system.

5.3. From lagging to leading

Another example use case of integration combines CAFM software ability to analyze“lagging” data and uses the same approach for on-line “leading” indicators comingform BMS.

The energy management in the CAFM software utilizes data from energy vendorinvoices. This data is available only for the building as a whole because the energymeter used for accounting is common for the building or even for the whole site.The BMS however allows to gather much more detailed data about the energyconsumption (the electricity consumption in particular) to the level of individualdevices such as AC drives (variable-frequency drives used for control of AC motorsof air conditioning units). Furthermore, the consumption data are far more recentthan consumption data received form the energy vendor once a month. The precisedata coming from the BMS together with analytical capabilities of the CAFM toolscan significantly improve the insight into the energy efficiency of buildings broughtto the level of individual devices, systems and rooms.

To make this seamless cooperation of two disciplines possible and bring similareasy-to-use tools as those available for the “lagging” indicator analysis to the fieldof “leading” data analysis, further steps in system integration must be taken.

5.4. Towards more semantics

Currently available data of the BMS at Masaryk University do not contain infor-mation about their meaning by default. Each data point is identified only by itsnetwork address according to BACnet protocol specification. Name of the data pointis designed to be understandable by human operators and is unsuitable for machineprocessing. Semantic information such as location of the sensor or measured quan-tity must be added manually for the purposes of analysis. This drawback makesdata analysis inflexible and prolongs the data analysis workflow. Ad-hoc approachto system integration is sufficient for experimental purposes such as testing newmethods of data analysis and provides valuable results concerning evaluation ofdifferent approaches, but prevents deployment of proposed methods for routine op-eration. Complexity of the system and lack of semantic information about gathereddata in fact prevents facility management staff to perform data analysis routinelyand on regular basis.

The necessity of ad-hoc approach to linking data from various sources also hidescomplex relations between data points, devices, environmental variables and otherfactors that influence building operation. As mentioned in the evaluation of exper-imental part of this article, to fully understand room temperature change patterns,we need to utilize large number of other indicators that are unavailable in current


setup. Some of them are not measured at all, some of them are not stored in archivaldatabase or they are available in information system that is not freely accessible.On the other hand, some additional data about building operation influencing tem-perature in examined rooms are gathered and stored by BMS. Even though, sizeand complexity of the system hides certain relations between operation data fromvarious sources which limits the human operators in meaningful usage of such datain the task of post-mortem operation analysis.

Typical example of such hidden relation is the link between state of pumps,motors and valves of central heating unit placed in the utility room in the basement.Heating unit is monitored and controlled by different part of the BMS/BAS thanthe local AC unit in the lecture room. The local AC controller has ability to controlthe valve on the heating radiator in the room. Actions of the local AC controller arethus influenced by current setup of central heating unit (if the central heating is off,controlling of the valve of the room radiator does not influence room temperature atall). The relation of central heating unit setup and operation of local AC unit cannotbe observed from the archive data itself. Some information about complex relationscan be derived by examining regulation algorithms and detecting used variables,input and outputs, or data points. Other relations are not described in the BMS orBAS itself at all because it is determined by physical installation of various devicesin the building. In the case of multiple central heating units, BMS/BAS does notcontain information concerning physical plumbing - it is impossible to determinewhich radiator valve is connected to which central heating unit.

5.5. Ontologies for intelligent buildings

In order to make easy and fast retrieval of all the related data from the BMS, on-tology model of relations between different elements in the building (devices, loca-tions, data points, affected variables, measured physical quantities, . . . ) is needed.Creation of such model is made possible by existence of BIM (Building Infras-tructure Modeling) data sources containing information about facilities of Masarykuniversity and physical installation and connection of all elements of the buildingequipment. In case of Masaryk University, we are talking about spatial databasesof “building passport” and “technology passport”. Extraction of information aboutphysical connection between devices allows us to create complete and complex modelof building environment. Construction of the model is challenging task that com-bines automated and semi-automated procedures (analysis of regulation algorithms,parsing electronic building documentation, extraction data from BIM sources) withexpert work of facility management stuff (definition of the model, development ofautomation tools, resolving conflicts, gathering BIM data).

5.6. Future applications

With the model available, advanced data analysis applications can be developed.Querying the database of system model will enable applications to gather informa-


Fig. 5. Schema of FM systems integration.

tion from all the data points containing data related to the current object of interest.Figure 5 presents scheme of BMS system enriched by additional data sources andadvanced applications. BMS serves as source of building operation data. Archiveserver is relational database optimized for transaction processing (OLTP). Archivedata are transferred to the data mart which serves as OLAP data store for businessintelligence applications. Data in the data mart are enriched by semantic informa-tion gathered from spatial databases of BIM (technology passport). Data from datamart can be also used by CAFM software tools in the same way as they use “lag-ging” data. BMS can additionally serve as source of another type of informationbesides archived data from the database. Events generated by the elements of theBMS and on-line sensor data are obtained by monitoring applications and ComplexEvent Processing engine. Those tools serve for detection of faults or anomalies andoutliers in the system behavior, allowing BMS operators to identify faulty parts ofthe system.

6. Conclusions

This article introduces interdisciplinary field of facility management and presentscontrast between “lagging” performance indicators such as data obtained from en-ergy vendors and “leading” indicators obtained directly from various building sys-tems integrated in BMS (Building Management System). While analysis of “lag-ging” indicators is well supported in current state-of-the-art CAFM (Computer-


Aided Facility Management) software, analysis of “leading” indicators becomeschallenging research topic with the spread of intelligent buildings.

Article proposes model and method for evaluation of room comfort based ondata obtained from heterogenous systems and data sources. Data from BMS, roomreservation system and weather station data provider were joined and resultingdata sets were processed using statistical and computational knowledge methods.The approach combines data obtained fro building automation system with otherfactors that are not taken into account in regulation algorithms. Application ofmachine learning methods aims to reveal complex relations between performance ofbuilding conditioning systems and evironmental and spatial factors such as weatherconditions and room orientation or number of windows.

Next, we elaborate on future work in the field of building operation data based onthe results provided by experimental analysis of room comfort in multiple lecturerooms at University campus of Masaryk University. We propose development ofontology model of building systems and devices relation that will be able to speedup the analysis workflow (selecting appropriate data, joining inputs from varioussources, processing by analytical engine) and provide new flexible and easy to useapplications and tools for facility management in large organizations.

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DATA ANALYSIS IN THE INTELLIGENT BUILDING ENVIRONMENT

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