WO2018122784A1 - Procédé et système de gestion de programme d'éclairage de lampes - Google Patents

Procédé et système de gestion de programme d'éclairage de lampes Download PDF

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Publication number
WO2018122784A1
WO2018122784A1 PCT/IB2017/058502 IB2017058502W WO2018122784A1 WO 2018122784 A1 WO2018122784 A1 WO 2018122784A1 IB 2017058502 W IB2017058502 W IB 2017058502W WO 2018122784 A1 WO2018122784 A1 WO 2018122784A1
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WIPO (PCT)
Prior art keywords
lamp
crime
data
aoi
subject
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PCT/IB2017/058502
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English (en)
Inventor
Jun-Jang Jeng
Charles Okey NJELITA
Jay Gupta
Sachin Gangwar
Suman Mahalanabis
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Tata Consultancy Services Limited
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Publication of WO2018122784A1 publication Critical patent/WO2018122784A1/fr

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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
    • H05B45/00—Circuit arrangements for operating light-emitting diodes [LED]
    • H05B45/10—Controlling the intensity of the 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
    • H05B47/11—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
    • H05B47/16—Controlling the light source by timing means
    • 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 disclosure herein generally relates to field of lighting systems and, more particularly to, managing lighting schedules for the lighting systems in an optimized manner using predictive and prescriptive analytics.
  • An existing method controls street lighting over a group of road segments, wherein a road class is dynamically assigned to each road associated to each road segment and traffic parameters determined for each road segment for a current time period.
  • a road class is dynamically assigned to each road associated to each road segment and traffic parameters determined for each road segment for a current time period.
  • the existing lighting system even though dynamic, is a responsive or reactive and changes its predefined lighting schedules based on real time feedback of current traffic conditions. Reactive lighting systems are slow and do not provide optimization in energy management. Further, the existing method limits providing lighting control at higher level (road segment levels) and does not provide granular control at further lower levels, effectively not providing an optimized lighting control.
  • Another existing method utilizes clustering techniques to derive lighting requirements for the street lights.
  • the clusters are defined from the location-based data and lighting requirements are defined for each of the clusters based on the analysis of the location based data.
  • the existing method is responsive or reactive and changes its predefined lighting schedules based on real time feedback of current traffic conditions, hence does not provide optimization in energy management. Further, the existing method limits providing lighting control at higher level (cluster level) not granular control at further lower levels, effectively not providing an optimized lighting control.
  • a method for managing lighting schedule of a plurality of lamps comprises extracting area data from a plurality of data sources for an Area of Interest (AOI), wherein the AOI is set up with the plurality of lamps.
  • the extracted area data comprises at least one of crime data providing crime rate, traffic density data, subject density data, and lamp map data corresponding to every lamp among the plurality of lamps of the AOI.
  • the method comprises performing predictive analysis on at least one of the crime data, the traffic density data and the subject data to predict spatio-temporal distribution of crime levels, traffic patterns and subject movement patterns associated with every lamp. Further, the method comprises generating a set of constraints associated with every lamp based on a plurality of regional factors associated with the AOI and the predicted spatio-temporal distribution of the crime levels, the traffic patterns and the subject movement patterns. Furthermore, the method comprises generating an optimized lighting schedule for every lamp based on the set of constraints and the lamp map data.
  • the optimized lighting schedule comprises a plurality of time slots of a day, On/Off status of every lamp for every time slot among the plurality of time slots, lamp type of every lamp, a brightness level of every lamp for every time slot and a corridor identifier corresponding to every lamp. Furthermore, the method comprises communicating the optimized lighting schedule to a controller to control every lamp in accordance with the optimized lighting schedule.
  • the controller comprises one of a centralized entity and a network of distributed entities.
  • a lighting management controller for managing lighting schedule of a plurality of lamps.
  • the lighting management controller comprises a prediction module configured to extract area data from a plurality of data sources for an Area of Interest (AOI), wherein the AOI is set up with the plurality of lamps.
  • the extracted area data comprises at least one of crime data, traffic density data, subject density data, and lamp map data corresponding to every lamp among the plurality of lamps of the AOI.
  • the prediction module is configured to perform predictive analysis on at least one of the crime data, the traffic density data and the subject data to predict spatio-temporal distribution of crime levels, traffic patterns and subject movement patterns associated with every lamp.
  • the lighting management controller comprises a constraint generator module configured to generate a set of constraints associated with every lamp based on a plurality of regional factors associated with the AOI and the predicted spatio-temporal distribution of the crime levels, the traffic patterns and the subject movement patterns.
  • lighting management controller comprises an optimization module configured to generate an optimized lighting schedule for every lamp based on the set of constraints and the lamp map data.
  • the optimized lighting schedule comprises a plurality of time slots of a day, On/Off status of every lamp for every time slot among the plurality of time slots, lamp type of every lamp, a brightness level of every lamp for every time slot and a corridor identifier corresponding to every lamp.
  • the optimization module is configured to communicate the optimized lighting schedule to a controller to control every lamp in accordance with the optimized lighting schedule.
  • the controller comprises one of a centralized entity and a network of distributed entities.
  • a non-transitory computer readable medium for managing lighting schedule of a plurality of lamps.
  • the non-transitory computer-readable medium stores instructions which, when executed by a hardware processor, cause the hardware processor to perform acts comprising extracting area data from a plurality of data sources for an Area of Interest (AOI), wherein the AOI is set up with the plurality of lamps.
  • the extracted area data comprises at least one of crime data, traffic density data, subject density data, and lamp map data corresponding to every lamp among the plurality of lamps of the AOI.
  • the acts comprise performing predictive analysis on at least one of the crime data, the traffic density data and the subject data to predict spatio-temporal distribution of crime levels, traffic patterns and subject movement patterns associated with every lamp.
  • the acts comprise generating a set of constraints associated with every lamp based on a plurality of regional factors associated with the AOI and the predicted spatio-temporal distribution of the crime levels, the traffic patterns and the subject movement patterns. Furthermore, the acts comprise generating an optimized lighting schedule for every lamp based on the set of constraints and the lamp map data.
  • the optimized lighting schedule comprises a plurality of time slots of a day, On/Off status of every lamp for every time slot among the plurality of time slots, lamp type of every lamp, a brightness level of every lamp for every time slot and a corridor identifier corresponding to every lamp.
  • the acts comprise communicating the optimized lighting schedule to a controller to control every lamp in accordance with the optimized lighting schedule.
  • the controller comprises one of a centralized entity and a network of distributed entities.
  • FIG. 1 illustrates an exemplary lighting system implementing a lighting management controller for managing lighting schedule of a plurality of lamps, according to some embodiments of the present disclosure
  • FIG. 2 illustrates a functional block diagram of the lighting management controller of FIG. 1, according to some embodiments of the present disclosure.
  • FIG. 3 is a flow diagram illustrating a method for managing lighting schedule of a plurality of lamps, in accordance with some embodiments of the present disclosure.
  • the embodiments herein provide method and system, alternatively referred as lighting system, for managing lighting schedule of a plurality of lamps set up in an Area of Interest (AOI).
  • the method provides granular control by generating an optimized lighting schedule for every lamp of the plurality of lamps set up in the AOI using predictive and prescriptive analytics.
  • the prescriptive analysis refers to optimization, which is based a set of constraints that are applied to an optimization objective function (objective function).
  • the objective function may be preset for the AOI based on current lighting requirements and electrical energy availability for the AOI.
  • the set of constraints are generated from spatio- temporal predictions, which are derived by analyzing area data of the AOI.
  • the area data may be obtained from a plurality of data sources.
  • the set of constraints also include a plurality of regional factors associated with the AOI such as climate conditions climate data, city event calendar data, any third party automated data and the like.
  • the area data comprises crime data providing crime rate, traffic density data, subject density data, and lamp map data corresponding to every lamp among the plurality of lamps set up in the AOI. Consideration of the area data along with the regional factors to generate the set of constraints enables authorities managing the light system to plan energy target subject to electricity budget.
  • the proposed method is applicable to any AOI or environment such as street lighting in an urban or semi- urban environment and may be extended to corridor lighting in metro sub stations, huge complexes and the like with refining the constraints in accordance with need of the AOI considered.
  • the method is scalable and may be easily expanded to manage lighting of a plurality of AOIs that are included within the lighting system.
  • the method is portable and may be implemented on any operating platform with minor modifications to adapt to the platform.
  • portability and scalability makes the proposed method easy to implement while providing time and cost efficient energy management.
  • the predictive component can be overridden to generate revised optimized lighting schedule when real time area data such as traffic density data or subject density data or the like are obtained from sensors at every lamp.
  • the real time data provides information on changes that have occurred over time after the current optimized lighting schedule was implemented.
  • the revised optimized lighting schedule takes into account the changes to modify the lighting schedule accordingly.
  • the method repeats the generation of optimized lighting schedules at predefined intervals, wherein the area data is newly obtained or extracted before generating the revised optimized schedule, automatically capturing any recent changes in the area data that have been updated in the data sources of the AOI.
  • FIG. 1 through FIG. 3 where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
  • FIG. 1 illustrates an exemplary lighting system 100 implementing a lighting management controller 102 for managing lighting schedule of a plurality of lamps, according to some embodiments of the present disclosure.
  • the lighting system 100 comprises the lighting management controller 102 managing optimized lighting schedules for a plurality of areas or plurality of AOIs (not depicted in FIG.l).
  • AOI 112 which is set up with a plurality of lamps LI to Lp as depicted.
  • a plurality of data sources such as data sources 106-1 through 106-n may maintain area data for one or more areas (AOIs), for example AOI 112.
  • the data sources 106-1 through 106-n may be a city database maintained by local authorities.
  • the data sources 106-1 through 106-n may be source of area data for the AOI providing the crime data providing crime rate, the traffic density data, the subject density data, and the lamp map data corresponding to every lamp among the plurality of lamps (Ll-Lp) of the AOI.
  • the crime data may comprise crime rate of a plurality of crime events recorded at every lamp (LI to Lp) with time stamp.
  • the traffic density data may comprise a traffic density time series providing density of traffic recorded at every lamp post (LI to Lp) for every time slot.
  • the time slot may correspond to a day (24 hours) divided in hour groups.
  • the subject density data may comprise a subject density time series providing density of subject recorded at every lamp post for every time slot.
  • the subject density herein may refer to human movement or animal movement or any other movement of interest recorded in the data base (data sources 106-1 through 106n) around every lamp (LI through Lp).
  • the plurality of lamps (LI to Lp), set up in the AOI 112 may be located in a corridor among a plurality of corridors CRD1 through CRDn), planned or designed for the AOI 112.
  • the lamp map data corresponds to information associated with every lamp such as the location of every lamp with reference to the corridors, type of the lamp and the like.
  • CRD1, CRD2 through CRDn-1 and CRDn also referred as corridor identifiers, identify a group of lamps set up in the respective corridor.
  • corridor CRD1 includes lamps LI to Lm
  • CDR2 includes lamps Lm+1 to Ln
  • CRDn-1 includes lamps Ln+1 to Lo
  • CRDn includes lamps Lo+1 to Lp.
  • the corridors may be streets identified by street numbers, wherein lamps set up on the streets for city lighting.
  • the corridors may be passages at metro stations or subways with lamps set up for passage lighting.
  • the lighting management controller 102 provides granular control of light schedule by generating the optimized lighting schedule for every lamp among the plurality of lamps (LI to Lp) set up in the AOI 112.
  • the optimization is based the set of constraints that are applied to an optimization objective function (objective function), which may be preset for the AOI 112.
  • object function objective function
  • the set of constraints are generated from the spatio-temporal predictions, which are derived by analyzing area data obtained from plurality of sources (106-1 through 106-n) maintaining the area data for the AOI 112.
  • the constraints also include the plurality of regional factors associated with the AOI 112 such as climate conditions or climate data of the AOI, AOI event calendar data, light policies and rules defined by the local authority of the AOI, any third party automated data and the like.
  • the optimized lighting schedule may be generated for predefined time range.
  • the optimized lighting schedule may be for coming week, for a fortnight and so on.
  • the lighting management controller 102 can be configured to generate the optimized lighting schedule.
  • the optimized lighting schedule is generated by utilizing a meta-heuristic optimization model which handles a set of direct and indirect constraints to arrive at an optimal light intensity forecast across corridors lighted with the lamps in various time slots such as hour groups.
  • the optimized light schedule can be viewed as a table comprising schedule for every lamp (LI to Lp) indicating a plurality of time slots of a day, On/Off status of every lamp for every time slot, the lamp type of every lamp, a brightness level of every lamp for every time slot and a corridor identifier (such as CRD1 though CDRn) corresponding to every lamp.
  • the lighting management controller 102 can be configured to communicate, the optimized lighting schedule, with a controller 114 corresponding to the AOI 112. Further, the controller 114 is configured to control every lamp in accordance with the optimized lighting schedule.
  • the controller 114 may be a centralized entity or a network of distributed entities comprising street light controllers.
  • the predictive component can be overridden to generate revised optimized light schedule when real time area data such as traffic density data or subject density data or the like are obtained from sensors at every lamp through one or more feedback sources 116.
  • the lighting management controller 102 can be configured to repeats the generation of optimized light schedules at predefined intervals in accordance with currently updated area data.
  • the components or modules and functionalities of lighting management controller 102 are described further in detail with reference to FIG. 2 and FIG. 3.
  • the data sources 106-1 to 106-n may be connected to a computing device 104 through a network 108.
  • the computing device 104 can include the lighting management controller 102.
  • the lighting management controller 102 may be embodied in the computing device 104 (not shown).
  • the lighting management controller 102 may be in direct communication with the computing device 104, as depicted in FIG. 1.
  • Data extracted or obtained from the plurality of data sources 106-1 through 106-n, such as area data, as well as data corresponding to the regional factors can be stored in a repository 110.
  • a network 108 connecting the computing device 104 and the data sources 106-1 through 106-n may be a wireless or a wired network, or a combination thereof.
  • the network 108 can be implemented as a computer network, as one of the different types of networks, such as virtual private network (VPN), intranet, local area network (LAN), wide area network (WAN), the internet, and such.
  • the network 108 may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), and Wireless Application Protocol (WAP), to communicate with each other.
  • HTTP Hypertext Transfer Protocol
  • TCP/IP Transmission Control Protocol/Internet Protocol
  • WAP Wireless Application Protocol
  • the network 108 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices. The network devices within the network 108 may interact with the lighting management controller 102 through communication links.
  • the computing device 104 which implements the lighting management controller 102 may be implemented in a workstation, a mainframe computer, a general purpose server, and a network server.
  • the repository 110 coupled to the lighting management controller 102 may also store other data such as the set of constraints and generated optimized lighting schedules, the regional factors predefined for the AOI 112 and any additional AOIs managed by the lighting management controller 102.
  • the repository 110 may be internal to the lighting management controller 102 (as depicted in FIG.2).
  • FIG. 2 illustrates a functional block diagram of the lighting management controller 102 of FIG. 1, according to some embodiments of the present disclosure.
  • the lighting management controller 102 includes or is otherwise in communication with one or more hardware processors such as a processor(s) 202, at least one memory such as a memory 204, and an I/O interface(s) 206.
  • the processor(s) 202 (hardware processor), the memory 204, and the I/O interface(s) 206 may be coupled by a system bus such as a system bus 208 or a similar mechanism.
  • the memory 204 further may include modules 210.
  • the modules 210 include a prediction module 212, a constraint generator module 214, an optimization module 216 and other modules (not shown) for implementing functions of the lighting management contollerl02.
  • the prediction module 212, the constraint generator module 214, the optimization module 216 may be integrated into a single module.
  • the module 210 can be an Integrated Circuit (IC), external to the memory 204 (not shown), implemented using a Field- Programmable Gate Array (FPGA) or an Application-Specific Integrated Circuit (ASIC).
  • FPGA Field- Programmable Gate Array
  • ASIC Application-Specific Integrated Circuit
  • the names of the modules of functional block in the modules 210 referred herein, are used for explanation and are not a limitation.
  • the memory 204 can also include the repository 110.
  • the hardware processor(s) 202 may be implemented as one or more multicore processors, a microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate data based on operational instructions assisting the execution of functions of the modules 210.
  • the processor(s) 202 is configured to fetch and execute computer- readable instructions stored in the memory 204 and communicate with the modules 210, external to the memory 204, for triggering execution of functions to be implemented by the modules 210.
  • the prediction module 212 is configured to extract the area data from the plurality of data sources 106-1 through 106-n for an Area of Interest (AOI) 112. Further, the prediction module 212 can be configured to perform predictive analysis on the area data.
  • the area data includes at least one of the crime data, the traffic density data, the subject density data in accordance with the lamp map data of the plurality of lamps (LI through Lp) that are set up in one or more corridors (such as CRD1 through CDRn) of the AOI 112.
  • the prediction analysis predicts the spatio-temporal distribution of crime levels, traffic patterns and subject movement patterns associated with every lamp, explained in conjunction with steps of a method 300 of FIG.3.
  • the constraint generator module 214 can be configured to generate the set of constraints associated with every lamp.
  • the constraint generator module 214 analyzes the plurality of regional factors associated with the AOI and the predicted the spatio-temporal distribution of the crime levels, the traffic patterns and the subject movement patterns to generate the set of constraints.
  • the optimization module 216 can be configured to generate the optimized lighting schedule for every lamp based on the set of constraints and the lamp map data, explained in conjunction with steps of the method 300 of the FIG.3.
  • the optimized light scheduled comprises the plurality of time slots of the day, On/Off status of every lamp for every time slot among the plurality of time slots, lamp type of every lamp, a brightness level of every lamp for every time slot, the corridor identifier (such as CRD1 through CDRn) corresponding to every lamp among lamps Lo through Lp.
  • the optimization module 216 can be configured to communicate, the optimized lighting schedule, with the controller 114 for controlling every lamp in accordance with the optimized lighting schedule.
  • the lighting management controller 102 can be configured to repeat the generation of the optimized lighting schedule to adaptively change the optimization schedule in accordance with real time changes occurred.
  • the real time changes may occur in the plurality of regional factors and/or the predicted the spatio-temporal distribution of the crime levels, the traffic patterns and the subject movement patterns associated with the AOI.
  • the area data changes such as the traffic density and the subject density can be captured by feedback sources 116 and provided to the lighting management controller 102. Any third party feedback sources may be used.
  • the I/O interface(s) 206 in the system 102 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface for defining regional factors such as light policies, budget constraints and the like.
  • the interface(s) 206 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, a printer and a display.
  • the interface(s) 206 enable the lighting management controllerl02 to communicate with other devices, such as the computing device 104, web servers, the controller 114, the feedback sources 116 and external databases.
  • the interface(s) 206 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite.
  • the interface(s) 206 may include one or more ports for connecting a number of computing systems with one another or to another server computer.
  • the I/O interface(s) 206 may include one or more ports for connecting a number of devices to one another or to another server.
  • the memory 204 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.
  • volatile memory such as static random access memory (SRAM) and dynamic random access memory (DRAM)
  • non-volatile memory such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.
  • the modules 210 may include routines, programs, objects, components, data structures, and so on, which perform particular tasks or implement particular abstract data types.
  • the modules 210 may include computer- readable instructions that supplement applications or functions performed by the lighting management controller 102.
  • the repository 110 may store data that is processed, received, or generated as a result of the execution of one or more modules in the module(s) 210.
  • FIG. 3 is a flow diagram illustrating the method 300 for managing lighting schedule of the plurality of lamps (LI to Lo), in accordance with some embodiments of the present disclosure.
  • the method 300 includes allowing the prediction module 212 to extract the area data from the plurality of data sources 106-1 through 106-n for the Area of Interest (AOI) 112.
  • the method 300 includes allowing the prediction module 212 to perform predictive analysis on the area data., which includes at least one of the crime data, the traffic density data, the subject density data in accordance with the lamp map data of the plurality of lamps (LI through Lp) that are set up in one or more corridors (such as CRD1 through CDRn) of the AOI 112.
  • the prediction analysis predicts the spatio- temporal distribution of crime levels, traffic patterns and subject movement patterns associated with every lamp.
  • the predictive analysis on the crime data providing crime rate to predict the spatio-temporal distribution of the crime levels associated with every lamp comprises determining a probability of every crime event among the plurality crime events at every lamp post for every time slot. The probability indicates belongingness of every crime event to each crime level among the crime levels.
  • the crime levels are predefined for the AOI by classifying the plurality of lamps into a plurality of cluster and assigning a crime level among the predefined crime levels for each cluster based on recorded crime data, which provides crime rate.
  • the prediction of the spatio-temporal distribution of the crime levels comprises tagging every crime event at every lamp post for every time slot to a crime level among the crime level, if the determined probability for a crime event is above a pre-defined probability threshold.
  • One among the many known classification techniques may be used for the prediction, for example Naive Bayes formula to calculate the probability that crime event at particular lamp location with given set of predictor values XI ... Xp belongs to class 1 (CI) or ( crime level 1 among m crime levels) among m classes is as follows:
  • Eq. 1 considers that the exact conditional probability is approximated by the product of the unconditional probabilities that those predictor values occur in the given class, overall, times the probability that a crime record belongs to that class (crime level) and the product of the unconditional probabilities that those predictor values occur across all classes.
  • the method 300 also includes allowing the prediction module 212 to perform the predictive analysis on the traffic density data to predict the spatio-temporal distribution of the traffic patterns associated with every lamp.
  • the traffic density data comprises the traffic density time series providing density of traffic recorded at every lamp post for every time slot.
  • the prediction comprises forecasting a time series for the traffic patterns associated with every lamppost using one of weighted time series moving averages and exponential smoothing based on forecast window estimate. The forecasting is based on the recorded traffic density time series.
  • the method 300 also includes allowing the prediction module 212 to perform the predictive analysis on the subject density data to predict the spatio-temporal distribution of subject movement patterns associated with every lamp.
  • the subject density data comprises the subject density time series providing density of subject recorded at every lamp post for every time slot.
  • the prediction comprises forecasting a time series for the subject movement patterns associated with every lamppost using one of the weighted time series moving averages and the exponential smoothing based on forecast window estimate. The forecasting is based on the recorded subject density time series.
  • the subject density and the traffic density are considered as weight (s) dependent, whereby two variables (subject movement patterns and traffic patterns) can be predicted using weighted time series moving averages. For example, moving average at time t, taken over N periods is given by,
  • x t is the observed response at time t around the lamp.
  • the method 300 includes allowing the constraint generator module 214 to generate the set of constraints associated with every lamp.
  • the constraint generator module 214 can be configured to analyze the plurality of regional factors associated with the AOI and the predicted spatio-temporal distribution of the crime levels, the traffic patterns and the subject movement patterns to generate the set of constraints.
  • the constraint generator module 214 provides the set of constraints that define lighting brightness criteria for every lamp. For example, illuminance level of a lamp can be set as per policy by the location or region standards such as whether the lamp is located in a city or small town.
  • illuminance level is also based on the lamp location (extracted from the lamp map data), where the lamp, if on busy street (higher traffic density and higher subject movement), requires higher brightness level while a lamp on lonely street (less traffic density and less subject movement) may require comparatively lower brightness level, subject to conditions of the crime levels for the location of the lamp.
  • busy street or the lonely street are examples for class of streets that may be taken onto consideration.
  • a lighting criteria may range from strictest; average illuminance level to the most relaxed; and minimum illuminance level.
  • the appropriate illuminance levels are chosen dependent on hour and typical volume of traffic flow.
  • the spatio- temporal predictions of the crime levels, the traffic patterns and the subject movement patterns along with regional factors such as city event calendars and the like define the set of constraints.
  • the method 300 includes allowing the optimization module 216 to generate the optimized lighting schedule for every lamp (LI to Lp) based on the set of constraints and the lamp map data.
  • the optimized light schedule for every lamp comprises the plurality of time slots of the day, On/Off status of every lamp for every time slot among the plurality of time slots, lamp type of every lamp, a brightness level of every lamp for every time slot, the corridor identifier (such as CRD1 through CDRn) corresponding to every lamp among lamps LI through Lp.
  • the method 300 comprises allowing the optimization module 216 to communicate (share) the optimized lighting schedule with the controller 114. The controller then can control every lamp in accordance with the optimized lighting schedule.
  • Generating the optimized lighting schedule for every lamp based on the set of constraints comprises minimizing a preset objective function to minimize tariff of electricity consumed by every lamp.
  • the minimizing of the preset objective function is based on Meta heuristic optimization techniques.
  • optimization formulation by minimizing the objective function is provided.
  • Pi current electricity price per hour/minutes for a city (AO I)
  • A Lamp life per voltage for every lamp set up in the city.
  • the objective function that needs to be optimized for every lamp, for an example street light lamp can be set.
  • xi ⁇ xi, X2. . . , xi-n, Xn > is a sequence of street lamps (LI to Lp).
  • *gi is assumed to be zero or constant since non-distributed lighting is considered.
  • the value of gi can vary due to movements.
  • L c is current illuminance (brightness level), and L t : target illuminance. In order to provide brightness uniformity in street lighting, all lamps of each group arrangements are adjusted to the same dimming level.
  • Luminous flux (U) can be calculated by dividing average luminance by minimum luminance
  • W is weight assigned to each light (lamp) depending on city policy (regional factors).
  • implementation algorithms are provided.
  • a local search algorithm is used to examine fifteen scenarios to find premier (or optimal) one which has minimum price while satisfying the constraints. Accordingly a street lighting optimization algorithm to generate premier scenario every hour is described below by way of an illustrious example, provided below:
  • a heuristic local search algorithm using equations provided can be used in known constraint solvers such as R or OptaPlanner to conduct search which starts from an initial solution and evolves into a better solution.
  • a single search path of solutions is used and not a search tree. At each solution in this path a number of moves on the solution are evaluated. Further, a most suitable move is selected to take the next step towards optimized.
  • the method 300 allows the lighting management controller 102 to repeat the generation of the optimized lighting schedule to adaptively change the optimization schedule in accordance with real time changes occurred.
  • the real time changes may occur in the plurality of regional factors and/or the predicted spatio-temporal distribution of the crime levels, the traffic patterns and the subject movement patterns associated with the AOI.
  • optimized light schedule provides a plan for next few days, weeks or months, the schedule can be changed in accordance with current requirement analyzed based on real time feedback of area data monitored and recorded for every lamp.
  • the proposed method 300 provides predictive, prescriptive (optimized) and reactive considerations for generating the optimized light schedule.
  • the illustrated steps of method 300 are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development may change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation.
  • the hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof.
  • the device may also include means which could be e.g. hardware means like e.g. an application- specific integrated circuit (ASIC), a field- programmable gate array (FPGA), or a combination of hardware and software means, e.g.
  • ASIC application- specific integrated circuit
  • FPGA field- programmable gate array
  • the means can include both hardware means and software means.
  • the method embodiments described herein could be implemented in hardware and software.
  • the device may also include software means.
  • the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
  • the embodiments herein can comprise hardware and software elements.
  • the embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc.
  • the functions performed by various modules described herein may be implemented in other modules or combinations of other modules.
  • a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
  • a computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored.
  • a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein.
  • the term "computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.

Abstract

L'invention concerne un procédé et un système de gestion d'un programme d'éclairage d'une pluralité de lampes installées dans une zone d'intérêt (AOI). Le procédé consiste à générer un programme d'éclairage optimisé pour toutes les lampes de la pluralité de lampes installées dans l'AOI. L'optimisation est basée sur un ensemble de contraintes qui sont appliquées à une fonction d'optimisation. L'ensemble de contraintes est généré à partir de prédictions spatio-temporelles, qui sont déduites par une analyse de données de zone de l'AOI. Les données de zone peuvent être obtenues à partir d'une pluralité de sources de données. L'ensemble de contraintes comprend également plusieurs facteurs régionaux associés à l'AOI. Le composant prédictif peut être neutralisé pour générer un programme d'éclairage optimisé révisé lorsque des données de zone en temps réel telles que des données de densité de trafic ou des données de densité de sujets, ou analogues, sont obtenues à partir de capteurs au niveau de toutes les lampes.
PCT/IB2017/058502 2016-12-30 2017-12-29 Procédé et système de gestion de programme d'éclairage de lampes WO2018122784A1 (fr)

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