Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N20/00—Machine learning
  • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N5/00—Computing arrangements using knowledge-based models
  • G06N5/04—Inference or reasoning models
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B15/00—Systems controlled by a computer
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
  • G06F1/26—Power supply means, e.g. regulation thereof
  • G06F1/28—Supervision thereof, e.g. detecting power-supply failure by out of limits supervision
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
  • G06F1/26—Power supply means, e.g. regulation thereof
  • G06F1/32—Means for saving power
  • G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N20/00—Machine learning
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N7/00—Computing arrangements based on specific mathematical models
  • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks

Definitions

• FIG. 1 A straightforward example of electric current consumption is depicted in Fig. 1.
• Current consumption in amperes over a period of several hours, using 1 minute intervals, is provided in the chart.
• the data may be collected by state of the art devices monitoring current consumption, for example the device discussed in U.S. patent application
• FIG. 3 a time chart of current consumption over time of an air conditioning roof top unit (RTU) that comprises of two compressors.
• the chart shows three discrete operational states: a) a low consumption mode at around 10A, that may be referred to as an 'idle' state, in which only the air handler or fan is operating; b) an intermediate 'on' state at around 25A, in which one compressor is operating; and, c) a high On' state of about 50A in which the two compressors are turned on together.
• RTU air conditioning roof top unit
• Figure 1 - is a time chart of current consumption versus time of a lighting circuit
• the network 410 may be wired, wireless or a combination thereof, it may be a local area network (LAN), a wide area network (LAN), a cellular network, and any other types of networks suitable for the transfer of data thereon.
• the SPPSs 470 sense currents flowing through power lines 430.
• PCDs single phase power consuming devices
• the processor 451 may be used to control the operations of monitoring device 450 by executing software instructions or code stored in the memory 453.
• the memory 453 may include one or more different types of storage such as hard disk drive storage, nonvolatile memory, and volatile memory such as dynamic random access memory. In some cases, a particular function as described below may be
• Such distributions are typically described by a wide standard deviation and low weight, and are typically apparent between two clearly defined states, for example, between 'single' and 'dual' compressor operational states for a RTU.
• the training module TM 452 filters out such state parameters and delivers to the classification module CM 454 only the parameters which are associated with desired classification states.
• CM 454 implementing a hysteresis condition, an even stronger condition of all consecutive measurements within a time period T may be found to belong to a specific probability state with a delta of at least some predefined percent from the probability of the adjacent state in order to decide to switch to a new state.
• the period of time T and predefined percentage, or other parameters are internal parameters of CM 454 that are associated with a specific model, or consumer type. In another embodiment, these can be automatically determined on a consumer type basis or on an individual consumer basis by the training module TM 452.
• the TM 452 may generate and transmit error messages, for example, when the TM 452 determines, for example, a too wide distribution, a low distribution weight, a too low average current, a too high average current, or an undesired ratio between state averages.
• the error messages are transmitted to one of the user device (UD) 480 to be displayed.
• a step of filtering out (or eliminating) one or more operational states takes place, for example, transitional operational states may be unnecessary for a particular PCD 420. While current readings are mentioned, other kinds of readings may be used, such as but not limited to, energy or power consumption, without departing from the scope of the invention.
• a PCD 420 is selected, either manually through a UI or API, or automatically by the monitoring device 450, for classification by CM 454.
• these parameters are provided to CM 454 from the training module TM 452.
• the parameters for a case of two Gaussian distributions where parameters jUon, OOn, JUidie, Oldie are the average and standard deviations of the operational states On' and 'idle' respectively.
• parameters for an 'Off operational state may have some low current threshold (i.e., close to zero but necessarily zero) used to detect an 'off operational state.

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• Engineering & Computer Science (AREA)
• Theoretical Computer Science (AREA)
• General Engineering & Computer Science (AREA)
• General Physics & Mathematics (AREA)
• Physics & Mathematics (AREA)
• Software Systems (AREA)
• Evolutionary Computation (AREA)
• Computing Systems (AREA)
• Data Mining & Analysis (AREA)
• Mathematical Physics (AREA)
• Artificial Intelligence (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Medical Informatics (AREA)
• Automation & Control Theory (AREA)
• Computational Linguistics (AREA)
• Remote Monitoring And Control Of Power-Distribution Networks (AREA)
• Testing And Monitoring For Control Systems (AREA)
• Supply And Distribution Of Alternating Current (AREA)

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• 2017
  • 2017-03-03 US US15/449,187 patent/US20170255864A1/en not_active Abandoned
  • 2017-03-04 JP JP2018565470A patent/JP2019513001A/ja active Pending
  • 2017-03-04 MX MX2018010319A patent/MX2018010319A/es unknown
  • 2017-03-04 WO PCT/IB2017/051270 patent/WO2017153880A1/en not_active Ceased
  • 2017-03-04 EP EP17762610.8A patent/EP3423789B1/en active Active

Patent Citations (5)

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