WO2013102881A1 - Système et procédé d'apprentissage de préférences d'éclairage d'utilisateur - Google Patents

Système et procédé d'apprentissage de préférences d'éclairage d'utilisateur Download PDF

Info

Publication number
WO2013102881A1
WO2013102881A1 PCT/IB2013/050091 IB2013050091W WO2013102881A1 WO 2013102881 A1 WO2013102881 A1 WO 2013102881A1 IB 2013050091 W IB2013050091 W IB 2013050091W WO 2013102881 A1 WO2013102881 A1 WO 2013102881A1
Authority
WO
WIPO (PCT)
Prior art keywords
user
preference
preferences
recited
estimated
Prior art date
Application number
PCT/IB2013/050091
Other languages
English (en)
Inventor
Saeed Reza BAGHERI
Dagnachew Birru
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 WO2013102881A1 publication Critical patent/WO2013102881A1/fr

Links

Classifications

    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
    • H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
    • H05B47/10—Controlling the light source
    • H05B47/105—Controlling the light source in response to determined parameters
    • H05B47/11—Controlling the light source in response to determined parameters by determining the brightness or colour temperature of ambient light
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
    • H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
    • H05B47/10—Controlling the light source
    • H05B47/105—Controlling the light source in response to determined parameters
    • H05B47/115—Controlling the light source in response to determined parameters by determining the presence or movement of objects or living beings
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
    • H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
    • H05B47/10—Controlling the light source
    • H05B47/155—Coordinated control of two or more light sources
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B20/00—Energy efficient lighting technologies, e.g. halogen lamps or gas discharge lamps
    • Y02B20/40—Control techniques providing energy savings, e.g. smart controller or presence detection

Definitions

  • the present invention relates to the field of lighting management and more particularly to a system and method for learning a user's preferences in natural and artificial lighting.
  • the present invention provides a method and system that provides for an integrated control of management of lighting conditions to provide preferred lighting conditions for individual users.
  • the present invention provides a method and system that provides for an integrated control of management of lighting conditions to reduce energy consumption while providing preferred lighting conditions for individual users.
  • the present invention provides a method and system that learns an individual user's preference in lighting conditions and applies the learned preferences to provide for satisfactory light conditions for individual users.
  • Figure 1 illustrates an exemplary system for learning user preferences in lighting in accordance with the principles of the invention.
  • Figure 2 illustrates a flow chart of a process for learning user preferences in lighting in accordance with the principles of the invention.
  • Figure 3 illustrates an exemplary system for implementing the processing shown in Figures 1 and 2.
  • Figure 1 illustrates an exemplary system 100 for managing a lighting environment in accordance with the principles of the invention.
  • an amount of natural lighting may be applied to an area in which a user 1 10 is located.
  • the amount of natural light entering the area may be determined based on the time of day, the direction (or orientation) of the window treatment system 120 (represented as a Venetian blind system), a size of a window (not shown) covered by the window treatment system 120, an amount of opening of blind system 120 and the angle of the slats (or blades) of the window treatment system 120.
  • lighting may be provided in the area (not shown) by an artificial lighting system 130.
  • the artificial lighting system may be one of an incandescent lighting system and/or a fluorescent lighting system.
  • Light sensors 140 and 150 may respectively detect and/or measure amounts of light emanating from inside sources (e.g., artificial light sources (130)) and outside sources (e.g., the sun (not shown)).
  • inside sources e.g., artificial light sources (130)
  • outside sources e.g., the sun (not shown)
  • an occupancy sensor 160 may be used to control when artificial light sources are activated. That is, the artificial light sources are only activated when a user 110 is within the area. Otherwise, the artificial light source is not activated and the light level within the area is only provided by any natural light that may be entering the area.
  • the outputs of light sensors 140 and 150 may be provided to a controller 170.
  • Controller 170 operating on the provide inputs, may control or provide command signals to change or adjust the position (height) of the blind system 120 and/or the orientation of the slats (cut-angle) of the blind system 120.
  • the controller may determine a light level provided by the artificial light sources(s) 130.
  • the user 1 10 may provide inputs to the controller 170. The inputs may present new settings or may represent the user's satisfaction, or dissatisfaction, with the settings.
  • Learning machine 180 receives input information from control 170 in addition to inputs provided by the user 110.
  • the inputs provided by the user 110 may include current position of the window treatment system, amount of natural and artificial light available, user settings for artificial light, time of day, day of week, month, season, etc.
  • Learning machine 180 determines a model of the preferences of the user based upon the provided inputs. For example, the learning machine 180 may determine a model of user preferences based on inputs such as a time of occupancy of the area by the user, position of the blind system 120 and the cut-angle of the slats of the blind system 120, direction of the window (not shown) covered by the blind system 120, the time of day, the day of the week and the season (e.g., summer, winter).
  • inputs such as a time of occupancy of the area by the user, position of the blind system 120 and the cut-angle of the slats of the blind system 120, direction of the window (not shown) covered by the blind system 120, the time of day, the day of the week and the season (e.g., summer, winter).
  • u [u l t ... nU ] (6)
  • n represents a corresponding maximum number for each of lamps, blinds, angle of blinds, users and sensors.
  • R ⁇ rj ( 10) wherein ⁇ represents values of individual user rewards. If a user is present (u j ⁇ 1), then in one embodiment individual rewards may be defined as:
  • individual rewards may be defined as:
  • the user preference record is kept in a database 77.
  • This database maintains a distribution of state-actions that satisfy users with maximum probability given a particular state, or
  • the database knows what blind height/angle and lamp intensity is preferred by users and so the database has recorded the action needed to take place (i.e. a combination of blind height/angle change and lamp intensity change) in order to maximize the probability of user satisfaction.
  • the user-machine interaction happens through three stochastic processes. Two out of three are controlled by the machine through a Gamma processes with different parameters and the third one is controlled by users through unknown process.
  • an unknown process is referred to, herein, as an arbitrary stochastic process by which a user interaction is governed.
  • This process is not known by the learning machine and is dependent on the user's interaction.
  • the stochastic process of user interaction e.g., satisfaction (likes), dis-satisfaction (dislikes), etc.
  • the learning machine described, herein performs well independent of the actual form of the process by which the user inputs likes and dislikes regarding the environmental settings determined based on the known processes described herein and the arbitrary process by which the user's likes and dislikes are entered and/or evaluated.
  • a Gamma process represents a random process with independent Gamma distributed increments.
  • Gamma distribution is a two-parameter family of continuous probability
  • Adjustment Interaction This happens through a Gamma process with parameters k a and ⁇ ⁇ . At each occurrence, the machine issues an action without a chance of being eligible for reward (i.e. zero reward) except possibly through user interaction (see below).
  • the learning machine makes a change to environment variables (e.g. dims the light), but it does not force the user to re-act by reporting whether he/she is happy (likes) or unhappy (dislikes) with this adjustment.
  • environment variables e.g. dims the light
  • time frame may be on the order of l/(fc m 0 m )].
  • the learning machine makes a change to environment variables (e.g. dims the light), and it forces the user to re-act by reporting whether he/she is happy or unhappy with this adjustment.
  • environment variables e.g. dims the light
  • a user can provide a voluntary report at any time. Upon such interaction, the initial zero reward is reported in an adjustment report immediately preceding this interaction.
  • Figure 2 illustrates a process chart for developing a user's lighting preference model in accordance with the principles of the invention.
  • step 210 a next interaction time or kind is determined
  • the interaction time may be preset to a periodic value from a previous interaction time or may be set in response to an interaction (e.g., detection of occupancy or a user changing a condition of the artificial lighting system or a user dissatisfaction report).
  • a current state of the conditions i.e., settings for natural and artificial lighting
  • actions are issued according to the current policy. For example, when occupancy is detected, commands may be sent to the window treatment system 120, to position the height of the blinds and/or the orientation of the slats based on a current time.
  • the occupancy sensor similarly may provide commands to the lighting system to activate the lighting system.
  • the lighting system may be activated to the last known setting or may be set according to a predetermined setting based on the window treatment setting.
  • Exemplary methods for determining blind orientation and slat cut-angle are disclosed in co-pending patent application entitled "A Method For Sharing Movement Adaptation Schedule To Prevent False Positive
  • a determination is made of a kind of interaction that has been detected i.e. Mutual, Adjustment. If the interaction is determined to be mutual then processing performs a Gamma process with parameters k m and 9 m , as previously described, wherein inputs provided by user 1 10 are obtained.
  • the user input may include the rewards settings discussed in Eqs. 1 1 and 12 and it may include a set point change initiated by the user. That is, the user inputs may, for example, include a change in a setting (lowering or increasing) the light output of the artificial light system.
  • the learning records are updated.
  • the updating of the learning records includes previously stored learning records obtained from data base (block 280).
  • the updating of the learning records may be based on time, day, week, month, season, etc.
  • the learning records may be correlated into periods of time, wherein the periods of time overlap by a known time period. This correlation of time allows for conditions wherein the user may provide an input at the exact same time each day.
  • a determination of when lighting preferences are sufficient may be determined by the user in response to indications provide by the user. For example, the user provides input regarding satisfaction or dissatisfaction ( ⁇ ) with each setting.
  • ⁇ satisfaction or dissatisfaction
  • initial setting of lighting and window treatment are established based on an initial lighting preference
  • an adjustment to either the lighting and/or window treatment setting may be made by the user.
  • the level of adjustment being outside a nominal tolerance level
  • the adjustment may be deemed that the user is not satisfied with the settings.
  • the user adjusted settings are maintained and established as the current settings. Processing then proceeds to step 210 to await a next period.
  • processing proceeds with a Gamma process with parameters k a and ⁇ ⁇ as previously described, and sets a next scheduled period for another adjustment. Inputs from a user 1 10 are obtained at block 225. Processing then proceeds as previously described.
  • the learning records are used to update the lighting/window treatment settings stored in data base 285.
  • the processing shown in Figure 2 may be performed for a plurality of time interval. Thus, is may be possible that a user's preferences may be acceptable or satisfactory for some time intervals and not others. Thus, the processing would continue for those intervals deemed not to be satisfactory.
  • the learning record contains a set L of observed triplets in the form of state, action, rewards (s, a, R).
  • L(s, a) ⁇ R 1 , R 2 ... ⁇ (14)
  • Equation 14 represents the observed rewards over the learning history.
  • an Epsilon-Greedy reinforcement method is used to update the state-action policy for each state that is encountered during the learning process as shown in Eq. (15).
  • m T , m D , m w , m,, m B , m. Q , m 0 and ⁇ ⁇ are the quantization numbers in the state dimensional sets T, D, W, I, B, Q, 0 and U, respectively.
  • the Epsilon-greedy method utilizes a greedy strategy with probability of 1 -epsilon and random strategy with probability of epsilon.
  • the greedy strategy refers to a best known strategy based on past trials and learning.
  • the quantization numbers refer to total number of distinct values a variable can take.
  • the convergence of the user preference policy may be measured by the consistency of the reward set over a given period of time. In one embodiment, the following condition can be used to assess learning convergence
  • T * is the minimum consistency time (in hours) for user satisfaction.
  • L(T * is defined as
  • the new state is created by issuance of a set of actions as governed by the policy described in Eq. 15.
  • This policy is, of course, initialized based on a user's own understanding of their preference and is evolved through the learning process.
  • the above-described methods according to the present invention can be implemented in hardware, firmware or as software or computer code that can be stored in a recording medium such as a CD ROM, an RAM, a floppy disk, a hard disk, or a magneto -optical disk or computer code downloaded over a network originally stored on a remote recording medium or a non- transitory machine readable medium that can be stored on a local recording medium, so that the methods described herein can be rendered in such software that is stored on the recording medium using a general purpose computer, or a special processor or in programmable or dedicated hardware, such as an ASIC or FPGA.
  • a recording medium such as a CD ROM, an RAM, a floppy disk, a hard disk, or a magneto -optical disk or computer code downloaded over a network originally stored on a remote recording medium or a non- transitory machine readable medium that can be stored on a local recording medium, so that the methods described herein can be rendered in such software that is stored on the recording medium using
  • the computer, the processor, microprocessor controller or the programmable hardware include memory components, e.g., RAM, ROM, Flash, etc. that may store or receive software or computer code that when accessed and executed by the computer, processor or hardware implement the processing methods described herein.
  • memory components e.g., RAM, ROM, Flash, etc.
  • the execution of the code transforms the general purpose computer into a special purpose computer for executing the processing shown herein.
  • FIG. 3 illustrates an exemplary system 300 for implementing the processing shown herein.
  • input devices 310 provide inputs to a processing unit 320.
  • the inputs are provided through a network 315.
  • the processing unit 320 includes an input/output unit 330 in communication with network 315, a processor 335 and a memory 340.
  • the input/output unit 330, the processor 335 and memory 340 are in communication via a network or bus 350.
  • the input/output unit receives inputs through the network 315 and provides the inputs to the processor 335 for subsequent processing.
  • the processor 335 accesses software, code, and/or instruction, stored in the memory 340, which when executed implements the processing shown herein.
  • the processing unit 320 (and the individual components described) represents structural means for implementing the processing shown herein.
  • the processor 335 may include logical units and arithmetic units that provide the structural means for implementing the processing shown herein.
  • the memory 340 may include the code or instruction which when accessed by the processor 335 causes the processor 335 to execute the processing shown herein.
  • the input/output unit 330 may further provide outputs determined by the processing unit 335 to one or more devices (printing/reporting device 360, second processing system 365 and/or storage device 370) to make the determined outputs available for review or subsequent processing.
  • the outputs may be provided through a network 380.
  • the input/output device 330 may be connected through a bus or network to additional memory unit 385, a second I/O device 390 and/or a processing unit 395 to receive additional inputs and/or provide outputs to other types of devices.
  • the system shown herein also provides that the code or instruction may be updated by downloading updated software instruction via one or more networks, wherein the updated software instruction is then stored in the memory 340.

Abstract

L'invention concerne un procédé et un système pour déterminer une préférence d'utilisateur dans la gestion de lumière d'une zone. Le procédé comprend les étapes consistant à collecter des données concernant une lumière artificielle et naturelle à l'intérieur de la zone, à collecter des données concernant les conditions météorologiques et à collecter les données collectées par rapport à des informations temporelles et géographiques associées à l'espace. Les données collectées sont traitées à l'aide de processus stochastiques pour estimer les préférences de l'utilisateur à un instant spécifique. Un éclairage artificiel (130) et un éclairage naturel sont ajustés sur la base des préférences d'utilisateur estimées. Des entrées d'utilisateur peuvent être fournies pour indiquer la satisfaction et/ou le mécontentement de l'utilisateur vis-à-vis de l'estimation. Des réglages de préférence d'utilisateur sont mis à jour et conservés lorsque l'utilisateur est satisfait des résultats.
PCT/IB2013/050091 2012-01-06 2013-01-04 Système et procédé d'apprentissage de préférences d'éclairage d'utilisateur WO2013102881A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201261583687P 2012-01-06 2012-01-06
US61/583,687 2012-01-06

Publications (1)

Publication Number Publication Date
WO2013102881A1 true WO2013102881A1 (fr) 2013-07-11

Family

ID=47739413

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2013/050091 WO2013102881A1 (fr) 2012-01-06 2013-01-04 Système et procédé d'apprentissage de préférences d'éclairage d'utilisateur

Country Status (1)

Country Link
WO (1) WO2013102881A1 (fr)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103759232A (zh) * 2014-01-21 2014-04-30 华南理工大学 一种依据自然光照的室内led照明调光方法
JP2015103453A (ja) * 2013-11-26 2015-06-04 大和ハウス工業株式会社 照明制御システム及び照明制御方法
EP3273752A1 (fr) * 2016-07-21 2018-01-24 Honeywell International Inc. Contrôleur de niveau de lumière de microclimat pour espaces occupés
WO2018074970A1 (fr) * 2016-10-18 2018-04-26 Plejd Ab Système et procédé d'éclairage pour commande automatique d'un motif d'éclairage
US10264639B2 (en) 2016-11-30 2019-04-16 Samsung Electronics, Co., Ltd. Apparatus and method for controlling light
WO2021105048A1 (fr) 2019-11-28 2021-06-03 Signify Holding B.V. Dispositif de commande pour entraîner une machine à automatiser des actions de commande d'éclairage et procédé associé

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0652690A1 (fr) * 1993-11-09 1995-05-10 Laboratoires D'electronique Philips S.A.S. Dispositif de commande automatique d'éclairage
US6263260B1 (en) * 1996-05-21 2001-07-17 Hts High Technology Systems Ag Home and building automation system
US20030160103A1 (en) * 2002-02-25 2003-08-28 Charles Guo Temperature control strategy utilizing neural network processing of occupancy and activity level sensing
WO2010079388A1 (fr) * 2009-01-07 2010-07-15 Koninklijke Philips Electronics N.V. Réseaux d'éclairage contrôlables intelligents et schémas conceptuels associés
US20110307112A1 (en) * 2010-06-15 2011-12-15 Redwood Systems, Inc. Goal-based control of lighting

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0652690A1 (fr) * 1993-11-09 1995-05-10 Laboratoires D'electronique Philips S.A.S. Dispositif de commande automatique d'éclairage
US6263260B1 (en) * 1996-05-21 2001-07-17 Hts High Technology Systems Ag Home and building automation system
US20030160103A1 (en) * 2002-02-25 2003-08-28 Charles Guo Temperature control strategy utilizing neural network processing of occupancy and activity level sensing
WO2010079388A1 (fr) * 2009-01-07 2010-07-15 Koninklijke Philips Electronics N.V. Réseaux d'éclairage contrôlables intelligents et schémas conceptuels associés
US20110307112A1 (en) * 2010-06-15 2011-12-15 Redwood Systems, Inc. Goal-based control of lighting

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
A. D. GALASIU; J. A. VEITCH: "Occupant Preference And Satisfaction With The Luminous Environment And Control Systems In Daylit Offices: A Literature Review", ENERGY AND BUILDINGS, vol. 38, 2006, pages 728 - 742
G. NEWSHAM; J. VEITCH; C. ARSENAULT; C DUVAL: "Effect of Dimming Control On Office Worker Satisfaction And Performance", IESNA ANNUAL CONFERENCE PROCEEDINGS, 2004, pages 19 - 41
G. R. NEWSHAM; M. B. C. ARIES; S. MANCINI; G FAYE: "Individual Control Of Electric Lighting In A Daylit Space", LIGHTING RES. TECHNOL., vol. 40, 2008, pages 25 - 41
T. MOORE; D. J. CARTER; A. SLATER: "A Field Study Of Occupant Controlled Lighting In Offices", LIGHTING RES. TECHNOL., vol. 34, 2002, pages 191 - 205

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2015103453A (ja) * 2013-11-26 2015-06-04 大和ハウス工業株式会社 照明制御システム及び照明制御方法
CN103759232A (zh) * 2014-01-21 2014-04-30 华南理工大学 一种依据自然光照的室内led照明调光方法
CN103759232B (zh) * 2014-01-21 2015-10-28 华南理工大学 一种依据自然光照的室内led照明调光方法
EP3273752A1 (fr) * 2016-07-21 2018-01-24 Honeywell International Inc. Contrôleur de niveau de lumière de microclimat pour espaces occupés
WO2018074970A1 (fr) * 2016-10-18 2018-04-26 Plejd Ab Système et procédé d'éclairage pour commande automatique d'un motif d'éclairage
US10624185B2 (en) 2016-10-18 2020-04-14 Plejd Ab Lighting system and method for automatic control of an illumination pattern
CN113966029A (zh) * 2016-10-18 2022-01-21 Plejd 公司 用于自动控制照明模式的照明系统和方法
US10264639B2 (en) 2016-11-30 2019-04-16 Samsung Electronics, Co., Ltd. Apparatus and method for controlling light
WO2021105048A1 (fr) 2019-11-28 2021-06-03 Signify Holding B.V. Dispositif de commande pour entraîner une machine à automatiser des actions de commande d'éclairage et procédé associé

Similar Documents

Publication Publication Date Title
WO2013102881A1 (fr) Système et procédé d'apprentissage de préférences d'éclairage d'utilisateur
Park et al. LightLearn: An adaptive and occupant centered controller for lighting based on reinforcement learning
Gunay et al. Development and implementation of an adaptive lighting and blinds control algorithm
EP3134779B1 (fr) Système et procédé de maintien de performance de système d'automatisation de bâtiment
US9706623B2 (en) Learning capable control of chaotic lighting
da Silva et al. Occupants interaction with electric lighting and shading systems in real single-occupied offices: Results from a monitoring campaign
Correia da Silva et al. Occupants’ behaviour in energy simulation tools: lessons from a field monitoring campaign regarding lighting and shading control
Guillemin et al. An energy-efficient controller for shading devices self-adapting to the user wishes
WO2017189780A1 (fr) Utilisation de programmes estimés et analyse de température de zone pour réguler un flux d'air
KR20090066107A (ko) 빌딩 에너지 관리 시스템 및 그 제어 방법
RU2597983C2 (ru) Управление окружающим светом
EP2982223B1 (fr) Système de collecte de la lumière du jour doté d'une protection pour empêcher une manipulation non autorisée
WO2018126034A1 (fr) Systèmes de commande destinés à la désinfection de systèmes d'éclairage et procédés de régulation des opérations de désinfection de systèmes d'éclairage
US20160234913A1 (en) Lighting system and method for controlling lighting system
US20120013434A1 (en) Wireless sensing module, wireless lighting controlling apparatus and wireless lighting system
WO2015189118A1 (fr) Réponse à la demande pour systèmes d'éclairage répartis en réseau
US20140318717A1 (en) Method for sharing movement adaptation schedule to prevent false positive indications in motion sensing based systems
Kent et al. Using support vector machine to detect desk illuminance sensor blockage for closed-loop daylight harvesting
Lindelöf et al. Bayesian estimation of visual discomfort
CN113485140A (zh) 基于物联网的智能家居调控方法、装置、设备及存储介质
JP2015118934A (ja) 周辺装置を制御して動作を切り替える動作検出装置、システム、方法およびコンピュータプログラム
Malavazos et al. Combined visual comfort and energy efficiency through true personalization of automated lighting control
Nagy et al. Illuminating adaptive comfort: dynamic lighting for the active occupant
CN110409987B (zh) 一种感光式百叶窗自适应控制方法及系统
López-Lovillo et al. How can a daylighting and user-oriented control system be configured? A state-of-the-art critical review

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 13705012

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 13705012

Country of ref document: EP

Kind code of ref document: A1