uilding heating technology in Smart Homesing PI System management tools�
an Vanus, Petr Vojcinak, Radek Martinek, Michal Kelnar,denek Machacek, Petr Bilik, Jiri Koziorek, Jan Zidek ∗
SB-Technical University of Ostrava, 17. listopadu 2172/15, Ostrava-Poruba 708 00, Czech Republic
eceived 26 October 2015; accepted 11 November 2015vailable online 17 December 2015
KEYWORDSAutomationtechnology;Building;Heating technology;Management tools;PI System;Smart Home
Summary For comfortable remote monitoring of some operational and technical functionsinside own Smart Home building, it is possible to use a lot of useful programmes and tools.However, not each programme or tool is suited to this purpose, or it does not offer requiredfunctionality. The aim of this paper is to describe using an appropriate software tool of PI Sys-tem for a real-time monitoring of acquired data from real technology parts located at a trainingcentre of the Moravian-Silesian Wood Cluster. Then a superior system including applications of PICoresight and PI ProcessBook is used for analysis and processing of these acquired data (e.g. byusing the Dynamic Time Warping method for specific technological quantities). Each applicationhas own advantages and disadvantages, which are evaluated in conjunction with possibilitiesof manipulating the data. In an experimental part, there are also applied a technological com-
odern buildings allow to unify monitoring and control-ing of building automation and electronic systems under aommon environment known as a Building Management Sys-em (BMS). Based on dynamic advancement of research andevelopment of new technologies within Smart Home, Smart
ome Care, Smart Metering, Smart Cities and their applica-ions using alternative sources and energy savings, there waseacted to these trends by building a platform for testing ofhese technologies in the MSWC building. In (Costanzo et al.,
icle under the CC BY-NC-ND license (http://creativecommons.org/
Figure 2 MSWC building — block scheme of the technologi-ct
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Building heating technology in Smart Home using PI System
2012), a system architecture for a load management insmart buildings is introduced and it enables an autonomousdemand side load management in the Smart Grid (Costanzoet al., 2012). In case of using wireless technologies, accord-ing to (Batista et al., 2013) they are becoming an importantasset in Smart Grid, particularly ZigBee devices. These smartdevices are gaining increased acceptance not only for build-ing and home automation, but also for energy management,efficiency optimisation, and metering services. In case ofmanaging energy consumption in the smart building, it canbe used Microgrid working as a local energy provider (Zhanget al., 2013). In this paper, some optimal scheduling ofenergy consumption in the smart buildings is studied usinga mixed integer linear programming.
Smart Home technology
The Smart Home building
A smart one-floor wooden house (hereafter Smart Home;technical alias: MSWC building; floor area: 12.1 m × 8.2 m;(Fig. 1), which was built at the FCE (i.e. Faculty of CivilEngineering, VSB-Technical University of Ostrava) site asa training centre of the Moravian-Silesian Wood Cluster(MSWC), contains modern technologies for automated con-trol of operational and technical functions (such as HVAC,heating sources, and regulation, etc.).
Features of used technologies
Technologies used in the MSWC building are divided into thetwo main categories (Fig. 2), thus: Building Heating Tech-nology (BHT) — includes heating technology in the MSWCbuilding (i.e. mutual connection among heat pump, solarpanel, accumulation tank, floor heating, boiler, ventilation,air conditioning; (Fig. 3). Building Automation Technology(BAT) — provides interoperability between BACnet and other
technologies to ensure automation of operational and tech-nical functions in the MSWC building (i.e. regulation, datavisualisation, data storage for databases, data processing)
Figure 1 MSWC building — view on this smart one-floorwooden house and installed technologies (i.e. a 2-U boreholeheat exchanger with the fifth pipe for measuring some uninflu-enced/influenced temperature profiles, and the solar collectorsystem).
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al parts (Building heating technology, and Building automationechnology).
ith the option to connect additional technologies via cor-esponding interfaces.
Both technologies (i.e. the BHT and the BAT) allow focus-ng on development, innovation and sample implementationn real applications, whereas the main aim of this researchithin the BAT is currently focused on implementing smart
ystems.
uilding heating technology (BHT)
he MSWC building is equipped with the BHT including autual connection of its main components (i.e. ground-ater heat pump, solar system with vacuum pipes with a
urface area of approximately 4 m2, accumulation tank, flooreating, boiler, ventilation, air-conditioning (Fig. 3) to the-U BHE (Fig. 2). Joint implementation of HST (Heat Stor-ge Technology), BHT and BAT therefore allows researchf the possibility of excessive heat accumulation into theurrounding rock massif.
uilding automation technology (BAT)
he control system, located in the MSWC building (Fig. 1),onsists of an automation sub-station of DESIGO PX PXC100-.D series (by SIEMENS) using a BACnet communicationrotocol for data exchange between its own components.he transfer medium used by DESIGO is Ethernet/IP. One ofhe basic characteristics is an open architecture of the sys-em, which supports individual components or other systemsith OPC, the Ethernet, and M-Bus (Fig. 4). The state andhange of monitored values is monitored by using a PXM 20Eontrol panel (Fig. 4).
All components of this control system — used for regu-ation of heating and cooling sources and all the securing,ontrol and auxiliary BAT elements — are located in oneentral location; this switchboard therefore contains com-unication modules for an HVAC unit, bus system, and data
ollection, specifically: temperatures (HST, BHT, BAT); rela-ive humidity and CO2 concentrations (BAT); pressures (HST,HT); heat consumptions (BHT); electricity consumptionsBHT). Let us add that the DESIGO PX PXC100-E.D automa-ion station is used in the MSWC building to control and
egulate ventilation, heating and air-conditioning (Fig. 4). Inddition to programmable control and regulation functions,his substation includes integrated functions of so-calledigher control (such as alarm control, time programmes,
116 J. Vanus et al.
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Figure 3 MSWC building — block sc
torage of measured data, remote access, access protec-ion for whole network with the option for defining userrofiles and categories, etc.). The complete technology isisualised using a DESIGO Insight software tool, which isodular, object-oriented and clearly designed software for
his control station. The visualisation application is dividednto a basic set and some extension modules in accordanceith needs of a certain technology (Vanus et al., 2013a,b).
ethodology of implementing PI Systemanagement tools
eatures of PI System
he PI System (hereinafter referred to as PI) is a designa-ion for a software tool provided by an American companyf OSIsoft, LLC. Individual programmes or programme groupsan also be operated separately. For example, PI is usedn business infrastructure for data and event managementn real time. The PI also enables data storage and search-ng for required trends, as well as analysis, distribution and
isualisation of the data from various data sources.
It can be subdivided into the following function blocks,hus: data source — block for control system connection;erver block; client block. The first block represents a
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e of the BHT part and the HST part.
hysical interconnection of the data sources with the controlystem or a PI Server when using a manual input, separateatabase systems and/or legacy data. The second describedlock is the PI Server, which is a software for processing ofhe transferred data. The PI Server receives these data inorm of so-called PI events, consisting of a time stamp, aalue or a status, and additional attributes. The third blocks a block marked as PI Clients (OSIsoft, 2014). They are soft-are programmes used by end users to view and analyse theata. These measured data are visualised using a desktop oreb applications in PI Coresight (Fig. 5).
Desktop applications include (a) PI DataLink; (b) PI Pro-essBook; (c) PI DataAccess. The Web applications includea) PI Web Parts; (b) PI Active View; (c) PI Coresight.
mplementation of the PI System in the MSWCuilding
n the MSWC building, the OPC server is used as a PI dataource, based on the BACnet technology and visualised usinghe DESIGO Insight software tool (Fig. 4). This OPC serverommunicates with a PI OPC interface, which forwards the
ata to the PI Server (Fig. 5). It communicates with a clienttation where individual applications are running. Thesepplications — connected to the PI — can be programmedy using tools of PI API, PI SDK, or their combination. The PI
Building heating technology in Smart Home using PI System management tools 117
Figure 4 MSWC building — block scheme of the BAT part (including communication modules for the BHT part).
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Figure 5 Block diagram illustrating data transfer from a
API (i.e. PI Application Programming Integration) and the PISDK (i.e. PI Software Development Kit) are dynamically con-nected libraries used by the applications to communicatewith the PI Server.
Methodology of the experimental part
Conceptualization
There is some comparison of the features between thesoftware applications of PI Coresight and PI ProcessBookin the experimental part of this paper. This comparison is
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C server (located in the MSWC building) to the PI Server.
onceptually focused on possibilities of a fast statisticalnalysis (FSA; used for the acquired data) and a graphicalisualisation (used for monitoring of the operational andechnical functions in the MSWC building). For a mutualomparison of calculated statistical values, there were cho-en measured temperatures (i.e. labels from BT01.08 toT01.13) inside the accumulation tank (Fig. 3), and tem-erature (i.e. BT07.08 label) measured at the inlet of theentilation subpart (Fig. 3).
A method based on dynamic programming or dynamicime warping (DTW) is useful in order to find an optimalime alignment, and to calculate a distance between a vec-or of unknown behaviour for a time-dependent measured
118 J. Vanus et al.
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igure 6 PI Coresight — table-based and graph-based summai.e. labels from BT01.08 to BT01.13, and BT07.08).
uantity, v(t), and a vector of a reference sample for theime-dependent measured quantity, vref(t), within a certainlosed time interval [a, b] (e.g. day, week, or month; alsoables 2—4). This analysis is valid for an idle operation mode,herefore it can be expected with similar values of theseistances at defining overlap time intervals.
sing the PI Coresight
he PI Coresight is a Web-based application (based on the‘thin client’’ model), operating via a Web browser. It pro-ides creating some trends and monitors, and analyzingnformation about ongoing processes in the MSWC building.
he PI Coresight allows: (a) to search and display time-seriesr other PI data (Fig. 6); (b) to create a process visualisa-ion with a possibility to record the data for further analysisFig. 6); (c) to display a PI ProcessBook screen (Fig. 5); (d) to
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igure 7 PI ProcessBook — time-dependent trends of measured aT01.08 to BT01.10, and BT07.08); compare with Fig. 6.
ions of measured and statistically processed temperature data
hare this screen with other authorised users (i.e. the usersho have access to the PI Coresight).
sing the PI ProcessBook
he PI ProcessBook allows not only a real-time instant accessnd a data visualisation, but also (in the past tense usingnteractive) graphical screens (Fig. 7). The data can beisualised through some trends, dynamic symbols, tables,umerical values for subsequent processing and analysis ofhe acquired data. These visualisation screens can be grad-ally changed and adapted to new conditions and needs ofsers. For a fast statistical data analysis, it is possible to
se a table (Fig. 7) including some basic statistical param-ters of the measured data source (i.e. Average, Minimum,aximum, Range, Standard deviation, Time interval, etc.;able 1).
nd statistically processed temperature data (i.e. labels from
Building heating technology in Smart Home using PI System management tools 119
Table 1 PI ProcessBook — table based summarisation of measured and statistically processed temperature data (i.e. BT01.08,BT01.09, BT01.10, and BT07.08).
Table 2 Using the DTW method — summarising calculated values of the Euclidean distances, i.e. time interval of day andtemperature sensors labelled as BT01.08, BT01.09, BT01.10, and BT07.08.
For recognition of operational and technical functions inSmart Home, a DTW method was used. According to an algo-rithm, described in (Chen et al., 2010; Vanus and Koziorek,2014), compressed unknown behaviour of the vector of mea-sured non-electrical (typically temperature) quantity, v(t)in ◦C, with a weights vector, w(t) in ◦C, has been graduallycompared with each reference sample behaviour of the mea-sured non-electrical quantity, vref(t) in ◦C, with a referenceweights vector, wref (t) in ◦C.
A matrix of Euclidean distances (at considering the closedtime interval), Da,b in ◦C, is calculated by using this formula,thus (Chen et al., 2010; Vanus and Koziorek, 2014):
Da,b ={
N∑i=1
[vref (t = b)i − v(t = a)i]2
} 12
=
√√√√ N∑i=1
[vref (b)i − v(a)i]2 (1)
For determining a degree of similarity for time-dependent trends of the measured temperature data (allin ◦C) in the accumulator tank (Fig. 3), relevant tempera-ture sensors labelled as BT01.08, BT01.09, BT01.10 (Fig. 6or Fig. 7), has been chosen. For a comparison with a differ-ent measured temperature, a relevant temperature sensor
labelled as BT07.08 and related to the inlet of the ventila-tion subpart (Fig. 3), has been also used. The first approachbased on using a mutual correlation method (related tocorresponding time-dependent temperature trends) was
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ejected, because it was not possible to secure the identi-al length (i.e. identical number of samples at an identicalime) of the measured data from different data sources. So,he second approach is based on using the DTW method toetermine the degree of similarity between correspondingamples.
esults and discussion
esults of using the DTW method
esults of usage of the DTW method are summarised in theollowing tables including calculated coefficients related tohe Euclidean distances (Eq. (1)), thus: Day (i.e. 10 May015; Table 2); Week (i.e. from 10 May 2015 to 17 May015; Table 3); Month (i.e. from 10 May 2015 to 9 June 2015;able 4).
iscussion
ll of the above used tools have their justification and use-ulness for the BMS. In terms of the overview of instantonditions for individual technologies with possible alerts tohreshold situations and alarms, the PI ProcessBook is veryseful. On the other hand, in terms of the possibility of easyccess to the acquired data and measured temperature val-es via some Web-based application or via a smart phone,he PI Coresight is sufficient and better than using the PI
rocessBook.
From the results of using the DTW method (Tables 1—3),here is an expected similarity between time-dependentrends for the temperature sensors of BT01.08, BT01.09, and
120 J. Vanus et al.
Table 3 Using the DTW method — summarising calculated values of the Euclidean distances, i.e. time interval of week (7 days)and temperature sensors labelled as BT01.08, BT01.09, BT01.10, and BT07.08.
Week — results are valid for date interval from 10 May 2015 to 17 May 2015.
Table 4 Using the DTW method — summarising calculated values of the Euclidean distances, i.e. time interval of month (30days) and temperature sensors labelled as BT01.08, BT01.09, BT01.10, and BT07.08.
Month — results are valid for date interval from 10 May 2015 to 9
T01.10 (Fig. 3), due to short Euclidean distances in orderf 10−4 ◦C or 10−5 ◦C.
Naturally, at the idle operation mode, there are longuclidean distances between a group of the temperatureensors of BT01.08, BT01.09, BT01.10 and the BT07.08 tem-erature sensor. The daily and weekly results (Tables 2 and 3)re comparable with the monthly result (Table 4), becausehis operation mode has been fixed for a long time andppropriate time intervals overlap.
onclusion
he aim of this paper was to describe a functionality ofechnologies of the BHT part and the BAT part, and an inter-onnection with a superior system in the form of the PIystem. It is able to remotely monitor the operational andechnical functions in the MSWC building.
From a practical viewpoint, possibilities of the PI Core-ight and the PI ProcessBook were compared and evaluatedn dependency on performing the real-time fast statisticalnalysis of the measured temperature data. To get morenformation about interactions among these data, the DTWethod was preferred.In the future, it is planned to use this method for ana-
yzing these interactions not only at a standard operationode, but also at non-overlap time intervals.
onflict of interest
he authors declare that there is no conflict of interest.
cknowledgements
his paper has been elaborated in the framework of theroject entitled ‘‘Opportunity for young researchers’’,
V
2015.
eg. no. CZ.1.07/4512.3.00/30.0016, which is supportedy Operational Programme Education for Competitivenessnd co-financed by the European Social Fund and the stateudget of the Czech Republic. This work is also partiallyupported by the Science and Research Fund 2014 of theoravia-Silesian Region, the Czech Republic. This researchas partially supported by Faculty of Electrical Engineeringnd Computer Science (VSB-Technical University of Ostrava),nder the SGS project with registration numbers of SP015/181 and SP 2015/154.
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