WO2011128883A2 - Système de contrôle d'énergie - Google Patents

Système de contrôle d'énergie Download PDF

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Publication number
WO2011128883A2
WO2011128883A2 PCT/IE2011/000024 IE2011000024W WO2011128883A2 WO 2011128883 A2 WO2011128883 A2 WO 2011128883A2 IE 2011000024 W IE2011000024 W IE 2011000024W WO 2011128883 A2 WO2011128883 A2 WO 2011128883A2
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WO
WIPO (PCT)
Prior art keywords
appliance
monitoring system
processor
energy consumption
appliances
Prior art date
Application number
PCT/IE2011/000024
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English (en)
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WO2011128883A3 (fr
Inventor
Antonio Ruzzelli
Gregory O'hare
Anthony Schoofs
Original Assignee
University College Dublin - National University Of Ireland, Dublin
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.)
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Publication date
Application filed by University College Dublin - National University Of Ireland, Dublin filed Critical University College Dublin - National University Of Ireland, Dublin
Publication of WO2011128883A2 publication Critical patent/WO2011128883A2/fr
Publication of WO2011128883A3 publication Critical patent/WO2011128883A3/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R22/00—Arrangements for measuring time integral of electric power or current, e.g. electricity meters
    • G01R22/06—Arrangements for measuring time integral of electric power or current, e.g. electricity meters by electronic methods
    • G01R22/10—Arrangements for measuring time integral of electric power or current, e.g. electricity meters by electronic methods using digital techniques
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D2204/00—Indexing scheme relating to details of tariff-metering apparatus
    • G01D2204/20—Monitoring; Controlling
    • G01D2204/24—Identification of individual loads, e.g. by analysing current/voltage waveforms
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00004—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the power network being locally controlled
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/10—The network having a local or delimited stationary reach
    • H02J2310/12—The local stationary network supplying a household or a building
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/70—Load identification

Definitions

  • the invention relates to monitoring of electrical energy in locations such as homes or offices.
  • US6816078 (US2003/0093390). This describes a sensor and an estimating means which learns based on a training set in order to estimate individual electricity consumption of appliances in operation. Fundamental and higher harmonics of total load current and their phase differences are monitored.
  • US6906617 describes an arrangement to determine instantaneous power consumption of an appliance to determine its status, according to a comparison with a characteristic power consumption pattern.
  • US2010/0280978 (published after our priority date) describes a system to collect waveform data for multiple appliances in order to generate power consumption data on a per-appliance basis.
  • WO2009081407 describes a process of detecting an electrical signature of an appliance and subsequently inferring statuses of appliances. Actions may be taken such as reducing power consumption to below a threshold.
  • the invention is directed towards providing an improved energy monitoring system with an ability to more accurately monitor usage by individual appliances. Summary of the Invention
  • an energy consumption monitoring system comprising:
  • a processor adapted to recognise appliances according to inputs from the sensor, and a user interface
  • processor is adapted to:
  • the processor is adapted to guide user inputs to assist with generating the profiles.
  • At least some profiles identify the associated appliance as one or a combination of resistive, inductive, or capacitive components. In one embodiment, at least some profiles identify predicted variation patterns for said components.
  • At least some profiles include parameter values for real power, reactive power, and power factor.
  • At least some profiles additionally include parameter values for peak current, RMS current, peak voltage, RMS voltage, and sampling frequency.
  • the artificial intelligence functions includes a neural network.
  • the neural network comprises an activation function in the form of a sigmoid function for training according to the profiles.
  • the processor is adapted to use a combination of sensory inputs to process energy readings enabling signature acquisition, system setup automation, system calibration, system monitoring, system control, and system optimization.
  • the processor is adapted to generate a user interface for profiling appliances, and to augment reliability of user inputs during system profiling
  • the processor is adapted to autonomously refine the system. In one embodiment, the processor is adapted to implement a control loop that feeds back appliance monitoring accuracy data to refine parameters of the artificial intelligence functions.
  • the processor is adapted to control the capture of power data including appliance start and stop activity to autonomously generate appliances signatures.
  • the processor is adapted to recalibrate an external appliance load monitoring system.
  • the processor is adapted to perform online profiling and processing of appliance data.
  • the processor is adapted to compute an appliance activity based on the sensor inputs, in which filters are associated with appliances, and to return a positive appliance activity when the combination of expected inputs is verified.
  • the processor is adapted to make appliance state information available on a communication medium, thereby avoiding need for software support on a host system, and reducing the provision of annotated data.
  • the processor is adapted to operate as an online or offline energy recommender system, which breaks out energy costs to per-appliance costs, and recommends appliances' replacements to reduce users' costs.
  • the processor is adapted to group energy users into groups of users. In one embodiment, the processor is adapted to group users having similar energy usage profiles, for example with the objective of making the groups of users appear as a single entity for energy providers, and allowing specific group energy tariffs.
  • the processor is adapted to perform the steps of:
  • the invention provides a computer program product comprising a computer usable medium having a computer readable program code adapted to perform processor operations of a system as defined above in any embodiment when executing on a digital processor.
  • Fig. 1 is a diagram illustrating context of an energy monitoring system of the invention
  • Fig. 2 is a diagram showing the architecture of a processing core of the system
  • Fig. 3 is a plot illustrating power as a function of time for operation of a number of appliances
  • Figs. 4(a), (b), and (c) are plots illustrating parameters for operation of the processing core
  • Fig. 5 is an example of sensory information captured next to a kettle
  • Fig. 6 is a plot illustrating the relationship between reactive and active power
  • Fig. 7 is a set of plots showing active power signatures for four appliances
  • Fig. 8 is diagram showing generation of appliance activity states
  • Fig. 9 is a diagram showing example vectors generated during training
  • Fig. 10 is a diagram illustrating hining of weights in the neural network
  • an energy monitoring system 1 comprises a non-intrusive sensor 2 and a processing core 3.
  • the sensor 2 comprises a current transformer (CT), a microprocessor and related circuitry, a radio unit, a power unit (either a battery or connector to the mains), and a receiver appliance connected to the processor.
  • the processor may comprise a local and/or a remote processor, such as a laptop or a PC or a server.
  • the sensor can be any of known sensors for measuring the various electrical parameters outlined in the description below.
  • processing core comprises:
  • An "appliance profiling” layer that guides user profiling of electrical appliances within a premises and that generates a database of unique appliance signatures.
  • An "activity recognition” layer that utilises the unique signatures and trains a machine learning technique, such as a recursive weighted distance between combination of signatures and the signal, an artificial neural network (ANN), or a Bayesian classifier, that is then employed to recognize appliance activities in real time.
  • ANN artificial neural network
  • a unique signature state information database used to store and retrieve profiled appliances for broad reuse and faster new profiling of similar appliances.
  • profiling
  • the appliance profiling layer guides user inputs to generate a unique appliance signature for each particular appliance. This is a one-off procedure per appliance, which can be either guided by the user or system-automated.
  • Used-guided profiling The system 1 requires the user to turn-on the appliance, wait for a few minutes while the system captures data to profile the appliance, and finally turn-off the appliance. In case the appliance has multiple states such as stand-by or cycles, the layer requires those states to also be profiled.
  • Automated profiling The system recognises a certain threshold variation of one or more parameters and triggers automatically the profiling phase.
  • the appliance profiling layer identifies key parameters provided by the electricity meter used for the generation of a unique appliance signature. These parameters can be transmitted to the processing unit either periodically or the transmission is triggered by variation of a threshold, namely throshold delta reporting. A train of those parameters is captured and stored into an appliance signature database.
  • an appliance can be classified as resistive, inductive, or capacitive type. Inductors and capacitors affect the power consumption by shifting the alternate current with respect to the alternate voltage.
  • Active power (also known as “real power”), when an appliance changes state. Capacitors delay the current with respect to the voltage while the opposite happens for inductors. Considering that the power is the multiplication of voltage and current, if voltage and current are shifted, the power transferred to the appliance is less.
  • Peak current (Pc) to further discriminate between appliances with a dissimilar current peak following a change of activity states (e.g. start-up, steady state, standby, power down).
  • Pc relates to the appliance circuit specifics, as it represents the maximum amount of energy the appliance allows before reacting.
  • RMS current that provides consumption information independently from the voltage given by the energy provider.
  • Peak voltage (Pv) and RMS voltage (Vrms) relate to the specific voltage provided when the signature is made. These are useful to reinforce the machine learning technique about the voltage provided at the moment of profiling.
  • the system 1 captures a train of the above parameters for a time period per appliance state change. Overall, the system identifies 6 inner appliance specifics to generate a unique appliance signature, which is the base to discriminate between multiple appliances activity.
  • signature length and the meter sampling frequency are key factors captured when profiling appliances. These parameters are key to translate signatures from dissimilar types of energy meters into a standard signature. In fact, when profiling appliances, the system 1 may generate signatures of dissimilar duration in order to capture diverse appliance power modes. Signature length and sampling frequency allow the profiling layer to avoid inconsistencies between signatures generated with meters at dissimilar sampling frequencies by translating signatures into a standard frequency before storing it in the relational database.
  • the basic element of the ANN is a neuron, which can be represented as a simple succession of mathematical operations, such as weight balancing, sum and an activation function. Each input of a neuron is balanced by a different weight and is then aggregated into an activation function that can be as simple as a step function, or a more complex function such as hyperbolic tangent.
  • the ANN consists of interconnected neurons, as shown in Fig. 2. It provides a balance between complexity and response time.
  • the first layer consists of input neurons i.e., neurons with one or more inputs connected to external or internal data.
  • the second layer consists of hidden neurons that have inputs connected to the outputs of the first layer and are not in direct relation with inputs and outputs of the ANN.
  • the third layer consists of output neurons that have inputs connected to the outputs of the hidden neurons. Output neurons represent the direct outputs of the ANN.
  • the connections between the layers and neurons can vary.
  • the input layer can be connected to the output of the ANN in order to provide a feedback informing the new input state about the previous output.
  • the system 1 uses a similar feedback mechanism to improve the accuracy by allowing the user to notify the system of an incorrect guess.
  • all weights are random. Weight coefficients of neurons are modified using the data from a training data set, based on combination of signatures. The data and corresponding weight modifications are propagated through the hidden layer and then the output layer.
  • the output signals generated by the ANN are then compared against the desired values, namely the target as given in the training data set. The difference between the target and the output layer of the network is called error delta ⁇ . This error is then back propagated to the hidden neurons and the input neurons and the weight of each neuron may be modified accordingly.
  • the calculation used to tune the weights is as follows:
  • the system 1 implements an automatic learning program (ALP) that allows autonomous training of the system.
  • ALP uses the generated signatures and creates a training data set with all possible combinations of appliance activity, which is then used to tune the neuron weights autonomously.
  • an important aspect of the ANN is an activation function, which shapes the output of the neurons.
  • the output of the network should correspond to one of the profiled appliances, after several empirical trials we adopted a general sigmoid function, which is close to a threshold function with smooth angles to allow some neuron uncertainty in case of errors within the order of 5% or more, to account for small variations of power delivered by the energy provider.
  • ANN approach ability to learn while running through mechanisms of error feedback from the user; the ability to handle multiple simultaneous appliance states.
  • a drawback of the prior ANN approach is the lengthy training process that may take some minutes for profiling of more than 15 appliances, or if some appliances have long signatures e.g. a washing machine with a multi-state signature.
  • the advantages of an MP A include: (1) avoiding the appliance recognition training phase; (2) simplified representation and management of appliance signature and recognition; (3) tuneable recognition control based on varaition of weights in the calcualtion of the distance.
  • An important part of the system 1 is the repository used for storing profiled appliances. Once a new appliance is profiled, the signature, together with some metadata relative to the appliance model and location, are stored locally and duplicated in a remote database.
  • the duplication allows the creation of a common repository of signatures used to train the ANN and share signatures with other users, namely Unique Signature State Information (USSID) database.
  • USSID Unique Signature State Information
  • the USSID consists of 6 main relational tables. Three main tables, namely captured parameters, physical and environmental relate directly to the signature. As certain types of appliances with either dissimilar models or from various manufacturers are likely to have dissimilar signatures, the table of physical data captures the specifics of the appliance.
  • the USSID database grows, it is necessary to provide techniques to present to the user an initial standard set of relevant signatures.
  • the system can provide a common list of appliance signatures based on user location (password protected). It is in fact common to have same appliance models concentrated within the same area or region (e.g. electric showers are very common in Ireland and UK while a certain HVAC model are more common in warmer countries).
  • the system 1 interface the user can then browse the list of appliance models in the area or search for other signatures should the appliance not be in the list.
  • the USSID system in the system 1 is implemented in an SQL-based relational database.
  • the environmental table provides information of surrounding conditions during measurements as this may affect the signature accuracy.
  • the USSED was designed with a broad use in mind such as a large number of signatures generated by contributors.
  • the database implements a "contributor" table that includes a confidence rate, which increases according to the reputation of the contributor. We envision that a reputation would increase based on collection of opinions from other users.
  • the USSID system can handle multiple signatures of the same appliance ED according to the "signature property" table. Similar to contributor reputation, the "energy meter” table stores the accuracy of the energy meter, which can be used to tune the appliance recognition algorithm. For example, in the system 1 this would enable testing the meter accuracy and associate accuracy levels to different activation functions.
  • appliances signatures can be acquired, real training data can be produced, and the system accuracy can be compared with real appliances' usage, without requiring human intervention.
  • a set of sensor nodes is attached to the appliances or to the outlet to which they are plugged. The system 1 obtains the appliance operating states and marks the data captured at the energy monitor with the appropriate appliances states.
  • sensory data are used to identify the operating state of an appliance, and include but are not restricted to:
  • the extra sensor/actuator nodes are removed and are not needed anymore. They may however be reused at a later stage for system re-calibration and system monitoring.
  • the system 1 is independent from the communication protocol used by the energy monitor, the unit used for testing transfers data via a ZigBee-based acquisition system to a gateway connected to a local machine, which connects to either a local or remote relational database for storage.
  • the system 1 system resides on the local machine and processes energy data as they arrive from the network.
  • the system 1 is able to firstly generate appliance signatures and then train an ANN to recognize appliance activities in real time. This starts with the appliance profiling phase, a one-off procedure that allows the system 1 to characterize appliances that the user wants to recognize.
  • the profiling will create a set of unique appliance signatures that will then be used for the real-time activity recognition.
  • the system records this into a dedicated table in a remote database.
  • An advantageous aspect is what parameters will contribute to the generation of a given signature.
  • the real power consumption can discriminate between appliances with dissimilar power consumption but may fail when appliance consumption is similar.
  • an appliance can be predominantly resistive, inductive, or capacitive.
  • AC alternating current
  • Inductors and capacitors affect the power consumption by shifting the alternating current with respect to the alternating voltage. In particular, capacitors delay the current with respect to the voltage, while the opposite happens for inductors.
  • the power is the multiphcation of voltage and current, if voltage and current are shifted, the power transferred to the appliance is less. This effect is captured by the active and reactive power components, which, in mathematical terms, correspond to real and imaginary parts respectively, as shown in Fig. 6.
  • appliances work through the real power (active), while the reactive power (passive) is due to the presence of storage elements in the appliance circuit (inductors or capacitors), does not work at the load and heats wires.
  • Pure resistive appliances show no shift of current and voltage, the reactive part is null and all the power is transferred to the load. In contrast, the larger the current/voltage shift the greater the imaginary component.
  • Reactive and active powers are key parameters to calculate the power factor, which is captured by the energy meter.
  • Equation 1 reports the relation between the active, reactive and power factor.
  • the real power is the first important constituent that can discriminate appliances of dissimilar consumption, as shown for example in Fig. 3.
  • the power factor can discriminate between appliances of resistive, capacitive and inductive types.
  • the peak current relates to the appliance circuit specifics, as it represents the maximum amount of energy the appliance allows before reacting.
  • the system 1 collects also RMS current that provides consumption information independently from the voltage given by the energy provider.
  • peak voltage and RMS voltage relate to the specific voltage provided when the signature is made. Overall, the system identifies 6 constituents to generate a unique appliance signature, which is the base to disCTiminate between multiple appliance activities.
  • Additional factors captured when profiling appliances are the signature length and the meter sampling frequency. These parameters are key to translate signatures from dissimilar types of energy meters into a standard signature.
  • users may generate signatures of dissimilar duration in order to capture diverse appliance power modes. For example, an electric oven presents an initial period of almost constant current draw followed by periodic deactivations when the set temperature is reached, as shown in Fig. 3.
  • the system 1 implements a simple function that translates signatures into a standard frequency before storing it in the relational database.
  • Figure 4 shows power signatures for four appliances of different lengths and standard sampling frequency of 1 value per minute.
  • each unique signature has an associated expiration date.
  • the system can inform the user of the necessity of renewing a signature either manually or automatically.
  • the system can also identify the appliance aging process by looking at parameter variations over time. Should the system identify repetitive anomalous signature variations, it may require a new signature to be generated.
  • Wn Wo - (h *Lr) (2) where Wn is the new Weight, Wo is the old weight and Lr is the learning rate sets to a constant value to avoid inconsistencies due to rapid weight changes.
  • the system 1 implements an automatic training program (ALP) that allows autonomous training of the system.
  • ALP uses the generated signatures and creates a training data set with all possible combinations of appliance activity, which is then used to tune the neuron weights autonomously.
  • an important aspect of the ANN is the Activation Function, which shapes the output of the neurons.
  • the output of the network should correspond to one of the profiled appliances, after several empirical trials we adopted a general sigmoid function, which is close to a threshold function with smooth angles to allow some neuron uncertainty in case of errors within the order of 5% or more e.g., to account for small variation of power delivered by the energy provider.
  • the system 1 in this manner generates comprehensive information concerning current energy consumption in a binding, breaking down for the user the consumption pattern, which may be as illustrated in Fig. 3.
  • FIG. 4a shows the real appliance activity, which was manually annotated and used for comparison against the output from the system 1.
  • Fig. 4b shows the raw output from the ANN in response to the input energy data. An appliance is considered active if the output from ANN is equal or greater than 1. The output coming from the system is then passed through a filter module, as shown in Fig.
  • Appliance filters are created for each appliance, and compute the appliance activity output based on the right combination of sensor activity outputs. This processing can be either done at the sensor node or at any other machine.
  • the appliance state information is sent to a machine (e.g. the one where the system 1 runs), as a form of either an XML file, or a flow of data on the serial line delimited by well-defined packet headers, or written in a database.
  • a machine e.g. the one where the system 1 runs
  • Making the appliance state information available as a form of a string of bytes on a serial port or any other communication media has the advantage that the annotation system does not require any software support on any machine. Energy monitoring systems just need to read the serial port and get the information (e.g. no need of extra software to write appliances states into an XML file or a database).
  • Appliances filters are either hard-coded in software, or selected by the user, such as with buttons or via a user interface.
  • appliances are activated and deactivated individually and signature vectors are taken and stored in a training set.
  • the system 1 adopts 6 input neurons matched with 6 hidden neurons, which performed adequately for the 6 inputted parameters used to generate the signatures: apparent power, real power, power factor, peak current, rms current, peak voltage.
  • the system 1 adopts a general sigmoid function, which is close to a threshold function with smooth angles to allow some neuron uncertainty in case of errors within the order of 5% or more e.g., to account for small variation of power delivered by the energy provider.
  • the training set is then used to tune the weights internal to the neural network.
  • the weights are an internal part of the neural network relative to each neuron.
  • parameters from the meter arrive periodically approximately every 5 seconds and intervals TO, Tl, T2...Each sample arriving from the meter is fed as an input to the trained ANN.
  • the ANN calculates the combination of appliances closest to the input values and provides a sequence of appliance IDs as an output.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

Un système de contrôle de la consommation d'énergie comprend un capteur électrique (2), un processeur (3) adapté pour reconnaître des appareils selon des entrées à partir du capteur, et une interface utilisateur. Le processeur génère, dans une phase d'apprentissage, un profil d'énergie d'appareil pour chacun d'une pluralité d'appareils, et utilise des fonctions d'intelligence artificielle pour identifier l'utilisation d'un appareil particulier selon des sorties du capteur et les profils d'appareils. Au moins certains profils identifient l'appareil associé comme un ou une combinaison de composants résistifs, inductifs ou capacitifs, et au moins certains profils identifient des modèles de variations prédites pour lesdits composants, et incluent des valeurs de paramètres pour une puissance réelle, une puissance réactive et un facteur de puissance.
PCT/IE2011/000024 2010-04-15 2011-04-15 Système de contrôle d'énergie WO2011128883A2 (fr)

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WO2014090969A3 (fr) * 2012-12-14 2014-08-14 University College Dublin - National University Of Ireland, Dublin Système permettant une désagrégation de charge robuste
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