WO2013035022A1

WO2013035022A1 - Activity monitoring for demand-controlled ventilation

Info

Publication number: WO2013035022A1
Application number: PCT/IB2012/054463
Authority: WO
Inventors: Bahaa Eddine Sarroukh; Luca TIBERI; Andre Melon Barroso; Paul Henricus Johannes Maria Van Voorthuisen
Original assignee: Koninklijke Philips Electronics N.V.
Priority date: 2011-09-06
Filing date: 2012-08-30
Publication date: 2013-03-14

Abstract

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.

Description

ACTIVITY MONITORING FOR DEMAND-CONTROLLED VENTILATION

FIELD OF THE INVENTION

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.

BACKGROUND OF THE INVENTION

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.

According to standard 62-1999 of the American Society of Heating,

Refrigerating and Air Conditioning Engineers (ASHRAE) recommends for example a variable ventilation rate of 15-20 cubic feet per minute per person (cfm/person) besides a fixed rate dependent on the area covered. In order to meet this standard, many systems are designed to ventilate under the assumption of maximum occupancy level. This approach results in over ventilation when the actual occupancy is actually smaller than assumed.

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.

Usually, 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. However, 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).

SUMMARY OF THE INVENTION

An object of the present invention is to provide an improved DCV system with enhanced responsiveness and better immunity to environmental factors.

This object is achieved by an apparatus as claimed in claim 1, a system as claimed in claim 8, a method as claimed in claim 11, and a computer program product as claimed in claim 12.

Accordingly, 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.

According to a first aspect, 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.

According to a second aspect which may be combined with the first aspect, 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. Using 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. Of course other suitable properties, such as (changes of) frequency, predetermined (changes of) signal patterns or the like could be used.

According to a third aspect which can be combined with any one of the first and second aspects, 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. According to a fourth aspect which can be combined with any one of the first to third aspects, 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.

According to a fifth aspect which can be combined with any one of the first to fourth aspects, 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. Thereby, the reference data can be adapted to environmental and/or measuring conditions provided in the observation space.

According to a sixth aspect which can be combined with any one of the first to fifth aspects, 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. This provides the advantage that an automatic reset procedure is provided if one or more people have entered or left the observation space.

It is noted that 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.

Further advantageous embodiments are defined below.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be described, by way of example, based on embodiments with reference to the accompanying drawings, wherein:

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

according to a second embodiment; and

Fig. 3 shows a flow diagram of a control procedure for a DCV system

according to a third embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS Various embodiments of the present invention will now be described based on a DCV system with motion sensor based control. In the embodiments use is made of a PIR (Passive Infrared) sensor to estimate the number of people inside an observation space (meeting room or office), in order to implement DCV.

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). To achieve this, 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.

It is noted that the ventilation control processor 20 with the memory 30 can be implemented as a separate device which could be provided in the observation space. As an alternative, 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.

According to the first embodiment, real-time estimation of the number of people in a meeting room or office (observation space) can be made based on the output of the motion sensors S 1 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. As an example, 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. In step S210, motion detection signals are collected from motion sensors of the observation space. Then, in 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). Finally, in 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. Here, 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.

In step S310, motion detection signals are collected from the motion sensors.

Then, in step S320, the number of pulses (i.e. detected movements) is counted over a certain time interval (e.g. 1 -minute blocks). In the subsequent 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. In 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. According to this method, 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

classification of the remaining points. In particular, 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.

In summary, 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.

While the invention has been illustrated and described in detail in the drawings and the foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive. The invention is not limited to the disclosed embodiments. From reading the present disclosure, other modifications will be apparent to persons skilled in the art. Such modifications may involve other features which are already known in the art and which may be used instead of or in addition to features already described herein. For example, motion can be detected by measuring change in speed or vector of an object or objects in the observation space or field of view. 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),

electromagnetic induction (inductive- loop detectors), and vibration (triboelectric, seismic, and inertia-switch sensors). Acoustic sensors can be based on electret effect, inductive coupling, capacitive coupling, triboelectric effect, piezoelectric effect, and fiber optic transmission. Thus, 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.

Variations to the disclosed embodiments can be understood and effected by those skilled in the art, from a study of the drawings, the disclosure and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality of elements or steps. As already mentioned above, the functions of the ventilation control procedure, e.g. as described in connection with the above embodiments of Figs. 2 and 3, may be implemented as software routines or computer programs which may be stored/distributed on a suitable medium such as an optical storage medium or a solid-state medium supplied together with or as a part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope thereof.

Claims

CLAIMS:

1. An apparatus comprising:

a) a receiving input for collecting motion detection signals from at least one motion sensor (S 1 to Sn) for sensing motion in an observation space;

b) a processor (20) for deriving at least one motion related property from the collected motion detection signals; and

c) a memory (30) for storing reference data linking said at least one motion related property with room occupancy numbers;

d) wherein said processor is adapted to compare said derived motion related property with said reference data to estimate a room occupancy number in said observation space.

2. The apparatus according to claim 1, wherein said motion detection signals are unfiltered raw output signals of said at least one motion sensor (SI to Sn).

3. The apparatus according to claim 1, wherein said at least one motion related property comprises 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 said processor (20) is adapted to compare said pulse count to said reference data to estimate the number of people present in said observation space.

4. The apparatus according to claim 3, wherein said processor (20) is adapted to create said reference data by applying a clustering, wherein each time block of the motion detection signal is assigned to a vector containing said pulse count of each of said at least one motion sensor (S 1 to Sn), and wherein each time block is assigned to a cluster based on a minimum Euclidian distance from a centroid of each cluster.

5. The apparatus according to claim 1, wherein said processor (30) is adapted to estimate said room occupancy number by taking into account an average number of movements detected over a whole measurement session and over a predetermined number of last minutes.

6. The apparatus according to claim 1, wherein entries of said reference date in said memory (30) are organized according to the number of motion sensors (SI to Sn) installed in said observation space and dimensions of said observation space.

7. The apparatus according to claim 1, wherein said processor (30) is adapted to initiate a reset procedure if the number of movements detected in one time block differs from the previous time block by more than a predetermined number.

8. A system comprising an apparatus according to claim 1 and a ventilation device, wherein a ventilation rate of said ventilation device is controlled by said estimated room occupancy number.

9. The system according to claim 8, further comprising said at least one motion sensor (SI to Sn).

10. The system according to claim 9, wherein said at least one motion sensor comprises a passive infrared sensor.

11. A method comprising:

a. collecting motion detection signals from at least one motion sensor (S 1 to Sn);

b. deriving at least one motion related property from the collected motion detection signals;

c. creating reference data linking said at least one motion related property with room occupancy numbers; and

d. comparing said derived motion related property with said reference data to estimate a room occupancy number in an observation space.

12. A computer program product comprising code means adapted to produce the steps of method claim 11 when run on a computing device.