EP4104016A1 - Modélisation prédictive pour fenêtres à changement de teinte - Google Patents

Modélisation prédictive pour fenêtres à changement de teinte

Info

Publication number
EP4104016A1
EP4104016A1 EP21754072.3A EP21754072A EP4104016A1 EP 4104016 A1 EP4104016 A1 EP 4104016A1 EP 21754072 A EP21754072 A EP 21754072A EP 4104016 A1 EP4104016 A1 EP 4104016A1
Authority
EP
European Patent Office
Prior art keywords
sensor
tint
sensor data
states
window
Prior art date
Legal status (The legal status 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 status listed.)
Pending
Application number
EP21754072.3A
Other languages
German (de)
English (en)
Other versions
EP4104016A4 (fr
Inventor
Jack Kendrick Rasmus-Vorrath
Nidhi Satyacharan TIWARI
Nitin Khanna
See Wai FU
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
View Inc
Original Assignee
View Inc
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 View Inc filed Critical View Inc
Publication of EP4104016A1 publication Critical patent/EP4104016A1/fr
Publication of EP4104016A4 publication Critical patent/EP4104016A4/fr
Pending legal-status Critical Current

Links

Classifications

    • 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
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor
    • E—FIXED CONSTRUCTIONS
    • E06—DOORS, WINDOWS, SHUTTERS, OR ROLLER BLINDS IN GENERAL; LADDERS
    • E06B—FIXED OR MOVABLE CLOSURES FOR OPENINGS IN BUILDINGS, VEHICLES, FENCES OR LIKE ENCLOSURES IN GENERAL, e.g. DOORS, WINDOWS, BLINDS, GATES
    • E06B3/00—Window sashes, door leaves, or like elements for closing wall or like openings; Layout of fixed or moving closures, e.g. windows in wall or like openings; Features of rigidly-mounted outer frames relating to the mounting of wing frames
    • E06B3/66—Units comprising two or more parallel glass or like panes permanently secured together
    • E06B3/67—Units comprising two or more parallel glass or like panes permanently secured together characterised by additional arrangements or devices for heat or sound insulation or for controlled passage of light
    • E06B3/6715—Units comprising two or more parallel glass or like panes permanently secured together characterised by additional arrangements or devices for heat or sound insulation or for controlled passage of light specially adapted for increased thermal insulation or for controlled passage of light
    • E06B3/6722—Units comprising two or more parallel glass or like panes permanently secured together characterised by additional arrangements or devices for heat or sound insulation or for controlled passage of light specially adapted for increased thermal insulation or for controlled passage of light with adjustable passage of light
    • E—FIXED CONSTRUCTIONS
    • E06—DOORS, WINDOWS, SHUTTERS, OR ROLLER BLINDS IN GENERAL; LADDERS
    • E06B—FIXED OR MOVABLE CLOSURES FOR OPENINGS IN BUILDINGS, VEHICLES, FENCES OR LIKE ENCLOSURES IN GENERAL, e.g. DOORS, WINDOWS, BLINDS, GATES
    • E06B9/00—Screening or protective devices for wall or similar openings, with or without operating or securing mechanisms; Closures of similar construction
    • E06B9/24—Screens or other constructions affording protection against light, especially against sunshine; Similar screens for privacy or appearance; Slat blinds
    • 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
    • G05B15/02—Systems controlled by a computer electric
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
    • H04L12/40—Bus networks
    • H04L12/407—Bus networks with decentralised control
    • H04L12/413—Bus networks with decentralised control with random access, e.g. carrier-sense multiple-access with collision detection [CSMA-CD]
    • E—FIXED CONSTRUCTIONS
    • E06—DOORS, WINDOWS, SHUTTERS, OR ROLLER BLINDS IN GENERAL; LADDERS
    • E06B—FIXED OR MOVABLE CLOSURES FOR OPENINGS IN BUILDINGS, VEHICLES, FENCES OR LIKE ENCLOSURES IN GENERAL, e.g. DOORS, WINDOWS, BLINDS, GATES
    • E06B9/00—Screening or protective devices for wall or similar openings, with or without operating or securing mechanisms; Closures of similar construction
    • E06B9/24—Screens or other constructions affording protection against light, especially against sunshine; Similar screens for privacy or appearance; Slat blinds
    • E06B2009/2464—Screens or other constructions affording protection against light, especially against sunshine; Similar screens for privacy or appearance; Slat blinds featuring transparency control by applying voltage, e.g. LCD, electrochromic panels
    • G—PHYSICS
    • G02—OPTICS
    • G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B5/00—Optical elements other than lenses
    • G02B5/20—Filters
    • G02B5/208—Filters for use with infrared or ultraviolet radiation, e.g. for separating visible light from infrared and/or ultraviolet radiation
    • G—PHYSICS
    • G02—OPTICS
    • G02F—OPTICAL DEVICES OR ARRANGEMENTS FOR THE CONTROL OF LIGHT BY MODIFICATION OF THE OPTICAL PROPERTIES OF THE MEDIA OF THE ELEMENTS INVOLVED THEREIN; NON-LINEAR OPTICS; FREQUENCY-CHANGING OF LIGHT; OPTICAL LOGIC ELEMENTS; OPTICAL ANALOGUE/DIGITAL CONVERTERS
    • G02F1/00—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics
    • G02F1/01—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour 
    • G02F1/15—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour  based on an electrochromic effect
    • G02F1/163—Operation of electrochromic cells, e.g. electrodeposition cells; Circuit arrangements therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]

Definitions

  • all the layers are deposited in the integrated deposition system where the glass sheet does not leave the integrated deposition system during deposition.
  • an isolation trench 120 is cut through TCO 115 and diffusion barrier 110. Trench 120 is made in contemplation of electrically isolating an area of TCO 115 that will reside under bus bar 1 after fabrication is complete (see FIG. 1A). This can be done to reduce (e g., avoid) charge buildup and coloration of the electrochromic device under the bus bar, which can be undesirable.
  • the reference to a transition between a bleached state and colored state is non-limiting and suggests only one example, among many, of an electrochromic transition that may be implemented. Unless otherwise specified herein (including the foregoing discussion), whenever reference is made to a bleached-colored transition, the corresponding device or process encompasses other optical state transitions such as non-reflective-reflective, transparent-opaque, etc. Further, the term “bleached” refers to an optically neutral state, for example, uncolored, transparent, or translucent. Unless specified otherwise herein, the “color” of an electrochromic transition is not limited to any particular wavelength or range of wavelengths. For example, the wavelength can be visible, or any other wavelength disclosed herein. As understood by those of skill in the art, the choice of appropriate electrochromic and counter electrode materials governs the relevant optical transition.
  • Such an energy management system in conjunction with large area electrochromic devices (e.g., an electrochromic window), can dramatically lower the energy consumption of a building.
  • electrochromic devices e.g., an electrochromic window
  • counter electrode layer 310 includes inorganic and/or solid material.
  • the counter electrode layer may include one or more of a number of different materials that serve as a reservoir of ions when the electrochromic device is in the bleached state.
  • the counter electrode layer may transfer some or all of the ions it holds to the electrochromic layer, changing the electrochromic layer to, e.g., the colored state.
  • the counter electrode layer colors with the loss of ions.
  • ion conducting layers for electrochromic devices having a distinct IC layer
  • suitable ion conducting layers include silicates, silicon oxides, tungsten oxides, tantalum oxides, niobium oxides, and/or borates. These materials may be doped with different dopants, including lithium. Lithium doped silicon oxides include lithium silicon-aluminum -oxide.
  • the ion conducting layer includes a silicate-based structure.
  • a silicon-aluminum-oxide (SiAlO) is used for the ion conducting layer 308.
  • the software can be based at least in part on, for example, internet protocols and/or open standards.
  • One example is software from Tridium, Inc. (of Richmond, Virginia).
  • One communication protocol used with a BMS is BACnet (building automation and control networks).
  • the BMS may be configured for such communication protocol(s).
  • the warning signal may be received by the BMS of the building or by window controllers configured to control the electrochromic windows in the building.
  • This warning signal can be an override mechanism that disengages window controllers from the system.
  • the BMS can then instruct the window controller(s) to transition the appropriate electrochromic device in the electrochromic windows 505 to a dark tint level aid in reducing the power draw of the cooling systems in the building at the time when the peak load is expected.
  • Wireless communication is used in the window controller for at least one of the following operations: programming and/or operating the electrochromic window 505, collecting data from the EC window 505 from the various sensors and protocols described herein, and/or using the electrochromic window 505 as a relay point for wireless communication.
  • Data collected from electrochromic windows 505 may include count data such as number of times an EC device has been activated, efficiency of the EC device over time, current, voltage, time and/or date of data collection, window identification number, window location, window characteristics, and the like.
  • the window characteristics may comprise characteristics of the tintable material (e.g., electrochromic construct), or of the pane (e.g., thickness, length and width).
  • the 3D model initialized by the 3D model system includes the exterior surfaces of the surrounding structures and other objects at the building site and the building stripped of all but walls, floors, and exterior surfaces.
  • the cloud -based clear sky module 820 can assign attributes to the 3D model to generate clear sky 3D models such as, e.g., one or more of a glare/shadow model, a reflection model, and/or a passive heat model.
  • the cloud-based systems can be in communication with each other and with other applications via the (e g., cloud) network, e.g., using application program interfaces (APIs).
  • APIs application program interfaces
  • the system architecture has a cloud-based 3D modelling system that can generate a 3D model (e.g., solid model, surface model, or wireframe model) of the building site using a 3D modelling platform
  • a 3D model e.g., solid model, surface model, or wireframe model
  • Various commercially-available programs can be used as the 3D modelling platform.
  • Rhino® 3D software produced by McNeel North America of Seattle Washington.
  • Another example of a commercially-available program is Autocad® computer-aided design and drafting software application by Autodesk® of San Rafael, California.
  • FIG. 12 is schematic illustration of an example of certain logic operations implemented by the clear sky module 820 to generate tint schedule information based at least in part on clear sky conditions.
  • the clear sky module applies the tint state assigned to at least one (e g., each) condition to the condition values and then applies the priorities from the priority data to determine the tint state for at least one (e.g., each) zone at a particular time.
  • the clear sky module could apply the priorities from the priority data to the condition values to determine the condition that applies and then apply the tint state for that condition to determine a tint state for at least one (e g., each) zone at a particular time interval
  • FIG. 12 is schematic illustration of an example of certain logic operations implemented by the clear sky module 820 to generate tint schedule information based at least in part on clear sky conditions.
  • the clear sky module applies the tint state assigned to at least one (e g., each) condition to the condition values and then applies the priorities from the priority data to determine the tint state for at least one (e.g., each) zone at a particular
  • the clear sky module includes a ray tracing engine that determines the directions of rays of sunlight based at least in part on different positions of the sun in the sky throughout a day of a year or other time period and determines the reflection direction and intensity from the location and reflective properties of the external surfaces of the objects surrounding the building. From these determinations, 3D projections of direct beam sunlight through the window openings in the 3D model can be determined. At 1560, the amount and duration of any intersection of the 3D projection of sunlight from the models and the 3D occupancy region is determined. At 1570, the conditions are evaluated based at least in part on the determined intersection properties at operation 1560.
  • values from Module D 2712 are provided to Module B 2710 in the form of raw and/or filtered values (e.g., signals) representative of present environmental conditions measured by one or more infrared (IR) sensors.
  • the raw or filtered values e.g., singals
  • the raw or filtered values are provided in the form of a filtered rolling median of multiple infrared sensor readings taken at different sample times, where at least one (e.g., each) reading is a minimum value of measurements taken by the one or more infrared sensors.
  • the ambient temperature readings may be communicated from one or more ambient temperature sensors located onboard an infrared sensor and/or a standalone temperature sensor of, for example, a multi sensor device at the building.
  • the ambient temperature readings may be received from weather feed (e.g., supplied by athird party such as a weather forecasting agency).
  • Module D 2712 includes logic to calculate filtered IR sensor values using a Cloudy Offset value and sky temperature readings (7k,) and ambient temperature readings from local sensors ( /', complicating / .) or from weather feed (7'êt ⁇ Y , and/or a difference, delta (A). between sky temperature readings and ambient temperature readings.
  • a Module D 2712 receives (and uses) raw sensor readings of measurements taken by two or more IR sensor devices at a building (e.g., of a rooftop and/or multi-sensor device), at least one (e.g., each) IR sensor device having an onboard ambient temperature sensor for measuring ambient temperature (7 ' perennial strig, / ,) and an onboard infrared sensor directed to the sky for measuring sky temperature (7 based at least in part on infrared radiation received within its field-of-view. Two or more IR sensor devices may be used, e.g., to provide redundancy and/or increase accuracy.
  • T eak minimum (T skyI , T sky2, ..) - minimum ⁇ T ambi , T amb2 ,...) - Cloudy Offset ( Eqn . 1)
  • Tcaic minimum (T sky i, 3 ⁇ 4? ,... ) - leather - Cloudy Offset ⁇ Eqn. 2)
  • the DNN submodule 2170b uses a DNN binary classifier that generates 8-minute weather forecasts using 6-minutes of history. Unlike univariate LSTM forecasting, the DNN binary classifier may not require to ran in real-time, alleviating computational load on existing hardware. To account for site-specific differences (in geo-location, seasonal variation, and continuously changing weather fronts), the DNN binary classifier can be ran overnight using two to three weeks of historical data, which is updated daily, dropping the oldest day and bringing in the most recent data in retraining the model at least one (e.g., each) night. Such rolling daily updates can increase a likelihood that the classifier adapts in keeping with the pace and qualitative nature of the changing weather conditions. Upon retraining, model parameter weights can be adjusted to receive new inputs for generating forecasts for the duration of the subsequent day.
  • the control signals for implementing the tint level for at least one (e.g., each) zone are transmitted over a network to the power supply in electrical communication with the device (s) of the tintable windows of the zone to transition to the final tint level at operation 2660 and the control logic can iterate for the next time interval returning to operation 2610.
  • the tint level may be transmitted over a network to the power supply in electrical communication with electrochromic device (s) of the one or more electrochromic windows to transition the windows to the tint level.
  • the transmission of tint level to the windows of a building may be implemented with efficiency in mind.
  • tint level For example, if the recalculation of the tint level suggests that no change in tint from the current tint level is required, then there may be no transmission of instructions with an updated tint level.
  • control logic may recalculate tint levels for zones with smaller windows more frequently than for zones with larger windows.
  • the profiles determined by Module E 2713 can be used to generate information about prior distribution of radiation levels occurring within a specified range over a given time frame at a given geographical location.
  • these “typical” profiles identified constitute a mixture of Gaussian (e.g., random normal) processes, one can quantify the certainty of forecasted sensor values occurring within a particular range as a function of the first (mean) and second (variance) moments of an underlying Gaussian process.
  • days closer to the present may be given a correspondingly heavier weight in averaging day-length time series sensor data across a rolling window of the recent past.
  • the weighted Barycenter averages of historical sensor data can be supplied for the duration of any downtime (e.g., required for repair or maintenance).
  • calculation of weighted Barycenter averages involves preprocessing and/or machine learning, e.g., to temporally align coordinates and/or minimize the distances between time series profiles used in generating an optimal set of mean values that reflects the requirements of the weighting scheme.
  • an appropriate preprocessing technique is Piecewise Aggregate Approximation (PAA), which compresses data along the time axis by dividing time series into a number of segments equal to a desired number of time steps before replacing at least one (e.g., each) segment by the mean of its data points.
  • PAA Piecewise Aggregate Approximation
  • barycenter averaging can use historical sensor data stored over a time frame to calculate pointwise weighted distance of at least one (e.g., each) time index (e.g., from sunrise to sunset) to generate a likely radiation profile for the following day.
  • historical sensor data over a time frame in the range of from 7 to 10 days can be used.
  • Barycenter averaging can use the same distance between time indexes for at least two days (e.g., each day) of the time frame e.g., at an interval of at least about 0.5 minute (min), lmin, 1.5min, 2min, or 3min. .
  • the training data is designed with model features associated with a subset of one or more (e.g., all) possible weather conditions at the site.
  • the training data may include blocks of historical data obtained over periods of time during which a subset of weather conditions occurred at the site.
  • the model is optimized for the subset of weather conditions.
  • training data for a model optimized for a Tule fog condition might use input features obtained during the winter months and further during periods when the Tule fog was present.
  • Models (e.g., all models) available for selection may be of the same, similar, or unrelated model types.
  • all of the models may be structured at least in part on artificial neural networks having the same or similar architecture, e.g., they may all be recurrent and/or convolutional neural networks with the same architecture.
  • some of the models have a first neural network architecture while others have a different neural network architecture.
  • one or move models are neural networks, while one or more others may be regression models, random forest models, and/or other model architectures (e.g., as disclosed herein). .
  • some or all of the models are feedforward neural networks.
  • tint prediction models use neural networks model architecture
  • their first layers may have (i) different numbers of nodes ( based at least in part on expected numbers of distinct input features) and/or (ii) different types of nodes.
  • At least one (e.g., each) available model may have an architecture and/or training approach that is specific for its own set of expected input features.
  • Suitable clustering algorithms may take different forms.
  • radiation profiles can be provided and compared with one another to generate point-wise distances.
  • the profiles can be naturally clustered into different groups that may be associated with different external (e.g., weather) conditions.
  • external e.g., weather
  • FIG. 27B depicts examples of characteristic radiation profiles from different clusters. This figure illustrates an example of characteristic profiles of radiation profiles in different clusters. The labeling is as follows: (1.) Sunny, (2.) Cloudy, (3.) Partially Cloudy, (4.) Mix Sunny/Partially Cloudy, (5.)
  • model selection logic may select a model to use for real time (e.g., immediate) or near term tint state determination.
  • the process of deciding which model to use in real time (or near real time) may depend on the immediate or anticipated conditions and/or the differences between the models that are available for selection.
  • the model selection logic may monitor input parameter sources for possible problems. If a failure is observed that has or will likely result in an input feature becoming unavailable for a currently executing model, the model selection logic may in real time (e.g., immediately or promptly) shift to a different model for which all the required input features are currently available.
  • a submodule for filtering input features is configured to perform a support vector regression, or more specifically, a linear kernel support vector machine.
  • This type of algorithmic tool can generate coefficients of all the available input parameters. The relative magnitudes of the coefficients can serve as quantitative indicators of the associated input parameters relative importance.
  • a feature filtering submodule may be embedded in a feature engineering pipeline used in preprocessing input to the neural network during model training. As an example, see FIG. 30 described below.
  • an error minimization routine is used to adjust the coefficients, e.g., so that the calculated radiation value generated by the function matches the actual radiation value that was measured (e.g., a photosensor value taken to generate the feature values).
  • the regression technique may use calculations employed by a support vector machine to classify labelled points. The process may eliminate those features that contribute the least to minimizing the error of predictions. Regardless of the specific technique employed, the process may generate a regression expression with coefficients for at least one (e.g., each) of the feature values.
  • the feature elimination process initially applies a regression to all potential input features, and through this process ranks the features based at least in part on coefficient magnitudes.
  • One or more putative input features with low magnitude coefficients may be filtered out.
  • the process can apply the regression again, but this time with a reduced set of putative input features, the set having been reduced by eliminating certain low ranking input features in the previous regression.
  • the process may be continued recursively for as many cycles as is appropriate to reach a desired number of input features. For example, the process may continue until a user-defined stop criterion or a requested number of remaining predictors (e.g., a threshold) is reached.
  • the resulting feature set can then be used to initialize the neural network (e.g., having the most performant input configuration).
  • the decision to re-initialize the model with a new configuration of input features may be made with respect to how well the existing input features perform on the same validation set of recent historical data.
  • support vector regression may be a suitable technique for filtering and/or eliminating putative input features, it may not be the only suitable technique.
  • Other examples can include random Forest regression, partial least squares, and/or principal component analysis.
  • an RFE process removes from about 20% to about 70% of the initial number of available features. In certain embodiments, an RFE process removes at least about 10%, 25%, 50% or 75% of the features. In certain embodiments, an RFE process removes from about 50 to about 200 features. As an example, there are initially 200 distinct input features and over the course of an RFE process, 100 (50%) of these features are filtered, to reduce the number input features to 100 features at the end of the process.
  • the input feature elimination is flexible in identifying features to filter. For example, in a given iteration, a feature of any type may be filtered.
  • a feature of any type may be filtered.
  • An elimination procedure may consider elimination some features at one time interval, other features at a different time interval (e.g., time interval or time step), still other features at a third time interval, and so on.
  • Some feature types may be preserved at more than one time interval.
  • the elimination procedure may eliminate features at least in part on the basis of feature type (e.g., a rolling photosensor mean value versus a rolling IR sensor median value) and/or at least in part on the basis of time increment (compared to the current time).
  • a linear-kernel Support Vector Machine (SVM) (or other similar technique) is executed to eliminate model features to a subset of the total number of potential features that would be used.
  • stochastic optimization using information theoretic metrics e.g., a Fisher Information metric
  • PCA is executed to eliminate model features to a subset of the total number of potential features that would be used.
  • two or more techniques may be used in combination, e.g., to eliminate model features to a subset of the total number of potential features that would be used.
  • leveraging third-party APIs to supervise training of models optimized for performance on pre-labeled training data may obviate (1) the 'cold-start' problem (e.g., insufficient data available), and/or (2) the need for human intervention during model curation.
  • supervised models that may be used in various combinations include: multilayer perceptrons, decision trees, regressions (e g., logistic regression, linear regression, and the like), SVMs, naive Bayes, and the like.
  • unsupervised clustering may be combined with a specialized weather model built in a “supervised” fashion.
  • control logic labels each sensor data with readings taken during that hour as “Sunny.”
  • the control logic can generate training data that can be used to train a specialized weather model for a “Sunny” weather condition with the sensor data labeled with “Sunny” (or a label identified with “Sunny”) including the labeled data taken during that hour.
  • the control logic can generate other training data sets for use in training other specialized weather models for other weather conditions such as “Rainy,” “T-Storms,” etc. using sensor data in the database that has been labeled with the other corresponding weather conditions.

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  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Radiation Pyrometers (AREA)
  • Circuit Arrangement For Electric Light Sources In General (AREA)

Abstract

L'invention concerne des systèmes, des appareils, des procédés et des supports lisibles par ordinateur non transitoires liés au contrôle de la teinte de fenêtres à changement de teinte, qui comprennent divers modules prédictifs, et des modules associés d'assurance de la qualité.
EP21754072.3A 2020-02-12 2021-02-11 Modélisation prédictive pour fenêtres à changement de teinte Pending EP4104016A4 (fr)

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US202062975677P 2020-02-12 2020-02-12
US202063075569P 2020-09-08 2020-09-08
US202163145333P 2021-02-03 2021-02-03
PCT/US2021/017603 WO2021163287A1 (fr) 2020-02-12 2021-02-11 Modélisation prédictive pour fenêtres à changement de teinte

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