WO2013035022A1 - Surveillance d'activité pour une ventilation commandée à la demande - Google Patents

Surveillance d'activité pour une ventilation commandée à la demande Download PDF

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Publication number
WO2013035022A1
WO2013035022A1 PCT/IB2012/054463 IB2012054463W WO2013035022A1 WO 2013035022 A1 WO2013035022 A1 WO 2013035022A1 IB 2012054463 W IB2012054463 W IB 2012054463W WO 2013035022 A1 WO2013035022 A1 WO 2013035022A1
Authority
WO
WIPO (PCT)
Prior art keywords
motion
processor
related property
reference data
observation space
Prior art date
Application number
PCT/IB2012/054463
Other languages
English (en)
Inventor
Bahaa Eddine Sarroukh
Luca TIBERI
Andre Melon Barroso
Paul Henricus Johannes Maria Van Voorthuisen
Original Assignee
Koninklijke Philips Electronics N.V.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Koninklijke Philips Electronics N.V. filed Critical Koninklijke Philips Electronics N.V.
Publication of WO2013035022A1 publication Critical patent/WO2013035022A1/fr

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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
    • F24F11/74—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2120/00—Control inputs relating to users or occupants
    • F24F2120/10—Occupancy

Definitions

  • the present invention relates to an apparatus, method, and computer program product for demand-controlled ventilation (DCV) of an observed space, such as a meeting room, office room or the like.
  • DCV demand-controlled ventilation
  • Air quality in buildings can be degraded by emissions from synthetic materials or occupants (e.g. ammonia, C02). In order to address such emissions a base rate of ventilation must thus exist, which is area dependent and occupancy dependent.
  • synthetic materials or occupants e.g. ammonia, C02
  • a base rate of ventilation must thus exist, which is area dependent and occupancy dependent.
  • ASHRAE Refrigerating and Air Conditioning Engineers
  • DCV A solution devised to meet ventilation standards while providing for better energy efficiency is DCV.
  • the idea consists of adapting the ventilation rates to the actual occupancy in the environment.
  • the energy savings from DCV result primarily from avoiding heating, cooling and de-humidifying fresh air in excess of what is needed to provide recommended ventilation.
  • occupancy levels in DCV systems are indirectly determined using
  • C02 sensors based on the assumption that people exhale C02 at constant and known rates.
  • C02 concentration in an indoor environment is a commonly used metric to assess air quality in a people occupied space.
  • accuracy of C02 sensors depends on a homogeneous flow of air in the monitored environment to ensure that readings in a specific location are representative. This homogeneity is difficult to assess and enforce given the presence of drafts and the overall chaotic dynamics of air motion. Variations in C02 concentration can also be slow depending on the volume of the room, number of occupants and ventilation rates. As a consequence, ventilation control is unable to act quickly on new events to improve energy savings (e.g. reducing ventilation after population in the room reduces).
  • An object of the present invention is to provide an improved DCV system with enhanced responsiveness and better immunity to environmental factors.
  • the proposed control scheme for DCV systems is based on motion information obtained from a motion sensor as normally used to control lighting fixtures in buildings. Reusing this infrastructure to control ventilation eliminates the need of adding extra sensors thus allowing for simpler and more cost-effective systems.
  • An attractive solution to DCV systems is thus provided because coverage can be easily checked and motion sensors are largely immune to environmental factors and quickly react to changes in occupancy.
  • the motion detection signals may be unfiltered raw output signals of the at least one motion sensor. This measure ensures that all motion-related aspects or properties of the signal are reliably detected.
  • the at least one motion related property may comprise a pulse count indicating the number of pulses detected in the motion detection signal during a time block of a predetermined time period and wherein the pulse count is compared (e.g. by the processor) to the reference data to estimate the number of people present in the observation space.
  • the number of pulses as an example of a suitable motion related property of the motion detection signals provides a useful basis for estimating room occupancy.
  • suitable properties such as (changes of) frequency, predetermined (changes of) signal patterns or the like could be used.
  • the reference data may be created (e.g. by the processor) by applying a clustering, wherein each time block of the motion detection signal may be assigned to a vector containing the pulse count of each of the at least one motion sensor, and wherein each time block is assigned to a cluster based on a minimum Euclidean distance from the centroid of each cluster.
  • This distribution of reference data ensures a reliable estimation of room occupancy passed on motion detection signals of different motion sensors.
  • the room occupancy number may be estimated (e.g. by the processor) by taking into account an average number of movements detected over a whole measurement session and over a predetermined number of last minutes (e.g. last 6 minutes). This measure ensures that estimation results are not blurred by isolated singular events.
  • entries of the reference data in the memory are organized according to the number of motion sensors installed in the observation space and dimensions of the observation space.
  • the reference data can be adapted to environmental and/or measuring conditions provided in the observation space.
  • a reset procedure may be initiated (e.g. by the processor) if the number of movements detected in one time block differs from the previous time block by more than a predetermined number.
  • the apparatus may be provided in a measuring device or in the ventilation device and may be implemented as a discrete hardware circuitry with discrete hardware components, as an integrated chip, as an arrangement of chip modules, or as a signal processing device or chip controlled by a software routine or program stored in a memory, written on a computer readable medium, or downloaded from a network, such as the Internet.
  • Fig. 1 shows a schematic block diagram of a control unit for a DCV system according to a first embodiment
  • Fig. 2 shows a flow diagram of a control procedure for a DCV system
  • Fig. 3 shows a flow diagram of a control procedure for a DCV system
  • PIR sensors or other motion sensors are now common devices present in buildings and used to save energy by preventing lamps of remaining unnecessarily on. This infrastructure can thus be used to implement an improved DCV system.
  • Fig. 1 shows a block diagram of a control unit or system according a first embodiment for controlling a DCV system.
  • a ventilation device or system (not shown) is controlled by estimating a room occupancy Np (i.e. number of people or animals present in the observed space). This estimated room occupancy Np is then supplied to the ventilation device or system so as to control ventilation (e.g. ventilation rate).
  • a plurality of motion sensors SI to Sn are provide to detect motion within the observed space.
  • Motion detection signals generated by the motion sensors S 1 to Sn are supplied to a respective receiving input of a ventilation control processing unit or processor 20 for processing the motion detection signals to derive at least one motion related property or characteristic, based on which the room occupancy or occupancy level can be estimated by using a reference dataset stored in an internal or external memory 30.
  • the ventilation control processor 20 with the memory 30 can be implemented as a separate device which could be provided in the observation space.
  • the ventilation control processor 20 and the memory 30 may be integrated with the ventilation device or system, so that only the motion sensors S 1 to Sn are arranged as separate parts.
  • real-time estimation of the number of people in a meeting room or office can be made based on the output of the motion sensors S 1 to Sn.
  • an (unfiltered) raw output of the motion sensors SI to Sn In order to obtain detailed motion information for reliable estimation of room occupancy, it is advantageous to use an (unfiltered) raw output of the motion sensors SI to Sn. Any data processing, like averaging, should thus be by-passed.
  • the number of people is estimated by measuring at least one specific data property (like pulse count in third embodiment) and refer this to a calibration dataset that depends on the number of sensors SI to Sn installed in the room or having the room in their filed of view.
  • the estimation algorithm can also take into account previous measurements during the measuring session.
  • the motion detection signals may be collected using one or more PIR sensors provided with digital output. The digital output of such sensors indicates the presence or absence of movement in the monitored spaces. It thus contains properties which are useful to estimate the number of people.
  • Fig. 2 shows a flow diagram of a control procedure according to a second embodiment for controlling a DCV system.
  • step S210 motion detection signals are collected from motion sensors of the observation space.
  • step S220 at least one specific motion related property (e.g. time pattern, frequency pattern, image pattern, frequency shift etc.) of the collected motion detection signals is measured.
  • the measured property is compared in step S230 to a reference or calibration dataset.
  • This dataset provides a link between the measured properties and an estimated room occupancy number under consideration of the environmental and/or measuring conditions (e.g. number of motion sensors, dimensions of the observation space and/or other criteria which might influence the estimation result).
  • step S240 the occupancy number in the observation space is estimated based on the comparison result.
  • Fig. 3 shows a flow diagram of a control procedure according to a third embodiment for controlling a DCV system.
  • the estimation of room occupancy is based on the number of pulses in the motion detection signal. This gives an indication of the room dynamics in a given interval of time.
  • step S310 motion detection signals are collected from the motion sensors.
  • step S320 the number of pulses (i.e. detected movements) is counted over a certain time interval (e.g. 1 -minute blocks).
  • a certain time interval e.g. 1 -minute blocks.
  • step S330 the value of each block is compared to a reference dataset for obtaining an estimation of the number of people present in the room during the period of time considered.
  • step S340 the number of people in the room (i.e. room occupancy number) is then estimated by taking into account the average number of movements detected in the 1 -minute time blocks over the whole measurement session and over e.g. the last 6 minutes.
  • Entries in the reference dataset are organized according to the number of sensors installed in the room and to the room dimensions.
  • the reference datasets are created by applying a clustering method using the K-means algorithm which is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. It attempts to find the centers of natural clusters in the data as well as in an iterative refinement approach.
  • each time block is assigned to a vector containing the pulse counting of each sensor in the room. In this multidimensional vectorial space the Euclidean distance to cluster of points with previously known associated room occupancy numbers determine the clustering
  • each block is assigned to one of the clusters based on the minimum Euclidean distance from the centroid or geometric center of each cluster.
  • the procedure of Fig. 3 may have an automatic reset procedure in case the number of movements detected in one time block is significantly different from the previous blocks. This might mean that one or more people have entered or left the room.
  • the above embodiments may be implemented in a DCV system by reusing a given sensor infrastructure for lighting control already present in the room. Such a reuse thus creates a synergy between lighting and ventilation systems by reducing implementation costs of the latter while extending the value of ownership of the former.
  • the procedures of Figs. 2 and 3 may be implemented as software routine controlling the processor 20 of Fig. 1 or any other computer system or computing device.
  • the present invention relates to an apparatus, method, system and computer program product for a demand-controlled ventilation (DCV) system, wherein a metric for the DCV system can be based on motion information obtained from a motion sensor as normally used e.g. to control lighting fixtures in buildings.
  • DCV demand-controlled ventilation
  • Motion can be detected by sound (acoustic sensors), opacity (optical and infrared sensors and video image processors), geomagnetism (magnetic sensors, magnetometers), reflection of transmitted energy (infrared laser radar, ultrasonic sensors, and microwave radar sensors),
  • the motion related property of the motion detection signal may as well be a change in frequency (distribution), a predetermined frequency (range), a predetermined pattern or change of pattern, a change in speed or vector of an object or objects, or other changes in the given motion detection signal.

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Fluid Mechanics (AREA)
  • Air Conditioning Control Device (AREA)

Abstract

La présente invention concerne un appareil, un procédé, un système et un produit-programme d'ordinateur pour un système de ventilation commandée à la demande (DCV), une métrique pour le système DCV pouvant être basée sur des informations de mouvement obtenues à partir d'un capteur de mouvement tel que celui normalement utilisé, par exemple, pour commander des installations d'éclairage dans des bâtiments.
PCT/IB2012/054463 2011-09-06 2012-08-30 Surveillance d'activité pour une ventilation commandée à la demande WO2013035022A1 (fr)

Applications Claiming Priority (2)

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US201161531162P 2011-09-06 2011-09-06
US61/531,162 2011-09-06

Publications (1)

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WO2013035022A1 true WO2013035022A1 (fr) 2013-03-14

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3084532A4 (fr) * 2013-12-20 2018-02-14 Enlighted Inc. Détection d'occupation
WO2018091202A1 (fr) * 2016-11-16 2018-05-24 Tridonic Gmbh & Co Kg Système et procédé de création de profils de présence aux fins de domotique
CN108881462A (zh) * 2018-07-03 2018-11-23 刘彪 一种基于班班通的教学功能室使用情况监控系统
DE102017221656A1 (de) * 2017-12-01 2019-06-06 Zumtobel Ag Bewegungserfassung von Objekten mittels Bewegungsmelder

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5779538A (en) * 1994-12-15 1998-07-14 Conseils Etudes Et Recherches En Gestion De L'air Method and device for adjusting the ventilation of premises
EP1136762A2 (fr) * 2000-03-23 2001-09-26 Kleenair Maintenance Services Limited Dispositif pour la commande automatique d'un système d'entretien d'air
US20050128067A1 (en) * 2003-12-11 2005-06-16 Honeywell International, Inc. Automatic sensitivity adjustment on motion detectors in security system
WO2006055334A1 (fr) * 2004-11-09 2006-05-26 Truveon Corporation Procedes, systemes et progiciels de climatisation d'immeuble
WO2006090546A2 (fr) * 2005-02-23 2006-08-31 Matsushita Electric Works, Ltd. Dispositif d'entree d'ordinateur et systeme de commande environnementale utilisant celui-ci
US20090065596A1 (en) * 2007-05-09 2009-03-12 Johnson Controls Technology Company Systems and methods for increasing building space comfort using wireless devices
US20110112692A1 (en) * 2009-11-10 2011-05-12 Jetlun Corporation System for remote control of packaged terminal air conditioner and heaters with wireless remote control systems

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5779538A (en) * 1994-12-15 1998-07-14 Conseils Etudes Et Recherches En Gestion De L'air Method and device for adjusting the ventilation of premises
EP1136762A2 (fr) * 2000-03-23 2001-09-26 Kleenair Maintenance Services Limited Dispositif pour la commande automatique d'un système d'entretien d'air
US20050128067A1 (en) * 2003-12-11 2005-06-16 Honeywell International, Inc. Automatic sensitivity adjustment on motion detectors in security system
WO2006055334A1 (fr) * 2004-11-09 2006-05-26 Truveon Corporation Procedes, systemes et progiciels de climatisation d'immeuble
WO2006090546A2 (fr) * 2005-02-23 2006-08-31 Matsushita Electric Works, Ltd. Dispositif d'entree d'ordinateur et systeme de commande environnementale utilisant celui-ci
US20090065596A1 (en) * 2007-05-09 2009-03-12 Johnson Controls Technology Company Systems and methods for increasing building space comfort using wireless devices
US20110112692A1 (en) * 2009-11-10 2011-05-12 Jetlun Corporation System for remote control of packaged terminal air conditioner and heaters with wireless remote control systems

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3084532A4 (fr) * 2013-12-20 2018-02-14 Enlighted Inc. Détection d'occupation
WO2018091202A1 (fr) * 2016-11-16 2018-05-24 Tridonic Gmbh & Co Kg Système et procédé de création de profils de présence aux fins de domotique
DE102017221656A1 (de) * 2017-12-01 2019-06-06 Zumtobel Ag Bewegungserfassung von Objekten mittels Bewegungsmelder
CN108881462A (zh) * 2018-07-03 2018-11-23 刘彪 一种基于班班通的教学功能室使用情况监控系统
CN108881462B (zh) * 2018-07-03 2020-10-23 刘彪 一种基于班班通的教学功能室使用情况监控系统

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