Install mode and cloud learning for smart windows

Abstract

A cloud learning system for smart windows is provided. The system includes at least one server configured to couple via a network to a plurality of window systems, each of the plurality of window systems having at least one control system and a plurality of windows with electrochromic windows and sensors, wherein the at least one server includes at least one physical server or at least one virtual server implemented using physical computing resources. The at least one server is configured to gather first information from the plurality of window systems, and configured to gather second information from sources on the network and external to the plurality of window systems. The at least one server is configured to form at least one rule or control algorithm usable by a window system, based on the first information and the second information, and configured to download the at least one rule or control algorithm to at least one of the plurality of window systems.

What is claimed is: 
     
         1 . A cloud learning system for smart windows, comprising:
 at least one server configured to couple via a network to a plurality of window systems, each of the plurality of window systems having at least one control system and a plurality of windows with electrochromic windows and sensors, wherein the at least one server includes at least one physical server or at least one virtual server implemented using physical computing resources;   the at least one server configured to gather first information from the plurality of window systems, and configured to gather second information from sources on the network and external to the plurality of window systems; and   the at least one server configured to form at least one rule or control algorithm usable by a window system, based on the first information and the second information, and configured to download the at least one rule or control algorithm to at least one of the plurality of window systems.   
     
     
         2 . The cloud learning system for smart windows of  claim 1 , further comprising:
 the at least one server configured to determine microclimate weather information from the first information, with the microclimate weather information accessible via a network connection to the at least one server.   
     
     
         3 . The cloud learning system for smart windows of  claim 1 , further comprising:
 the at least one server configured to host a social network based on the plurality of window systems; and   the at least one server configured to collect and offer access to a plurality of rules or control algorithms from or for the plurality of window systems, as a function for the social network.   
     
     
         4 . The cloud learning system for smart windows of  claim 1 , further comprising:
 the at least one server configured to identify manufacturing or aging variances in the plurality of windows of the plurality of window systems based on use of the first information over a span of time.   
     
     
         5 . The cloud learning system for smart windows of  claim 1 , further comprising:
 the at least one server configured to generate a probabilistic shade model applicable to the plurality of window systems, based on the first information and based on the second information including real-time weather information regarding cloud cover, and based on a geometric model for at least one of atmospheric clouds, ground clutter, building shapes, or building profiles.   
     
     
         6 . The cloud learning system for smart windows of  claim 1 , further comprising:
 the at least one server configured to compare operations, control algorithms or rules of differing ones of the plurality of window systems and configured to derive a recommended operation, control algorithm or rule for one of the plurality of window systems based on such a comparison.   
     
     
         7 . The cloud learning system for smart windows of  claim 1 , further comprising:
 the at least one server configured to determine a classification of a user of one of the plurality of window systems, based on the first information; and   the at least one server configured to determine a recommended operation, control algorithm or rule for the one of the plurality of window systems based on the classification of the user.   
     
     
         8 . A smart window system with cloud learning, comprising:
 a plurality of windows, each having at least one electrochromic window and at least one sensor;   at least one control system in or coupled to the plurality of windows, the at least one control system configured to couple to a network and configured to couple to at least one server via the network; and   the at least one control system configured to upload information relating to the plurality of windows, to the at least one server and configured to download at least one rule or control algorithm from the at least one server, wherein the at least one server is configured to gather information from a plurality of window systems and from sources on the network external to the plurality of window systems to analyze and to form therefrom the at least one rule or control algorithm.   
     
     
         9 . The smart window system with cloud learning of  claim 8 , wherein:
 the information relating to the plurality of windows includes information from light sensors of the windows;   the at least one rule or control algorithm is based on comparison of the information from the light sensors of the windows and weather information obtained by the at least one server; and   operation of at least one of the plurality of windows is modified responsive to downloading the at least one rule or control algorithm.   
     
     
         10 . The smart window system with cloud learning of  claim 8 , wherein:
 the information relating to the plurality of windows includes light sensor information and user input information, which the at least one server applies to classifying a user of the window system;   the at least one rule or control algorithm is based on a classification of the user; and   the at least one control system is configured to adjust transparency of the at least one electrochromic window of at least one of the plurality of windows in accordance with the at least one rule or control algorithm.   
     
     
         11 . The smart window system with cloud learning of  claim 8 , wherein:
 the at least one sensor includes light and temperature sensors; and   the at least one rule or control algorithm is based on microclimate analysis performed by the at least one server using the information relating to the plurality of windows, from the light and temperature sensors.   
     
     
         12 . The smart window system with cloud learning of  claim 8 , further comprising:
 the at least one control system configured to download the at least one rule or control algorithm responsive to direction from an application on a user device, cooperating with the at least one server.   
     
     
         13 . The smart window system with cloud learning of  claim 8 , further comprising:
 the at least one control system having a mode configured to operate transmissivity of each of the plurality of windows in accordance with a plurality of rules or a control algorithm, which is modifiable by the at least one rule or control algorithm.   
     
     
         14 . The smart window system with cloud learning of  claim 8 , further comprising:
 the at least one control system configured to generate an environmental model individualized to each of the plurality of windows, based on information from the at least one sensor of each of the plurality of windows and based on weather information from the at least one server or from the sources on the network, the environmental model including a shade model for each of the plurality of windows; and   the at least one control system configured to adjust transmissivity of the at least one electrochromic window of each of the plurality of windows, based on the environmental model.   
     
     
         15 . A method for operating a smart window system with cloud learning, comprising:
 receiving, at at least one server, information from a plurality of window systems and information available on a network external to the plurality of window systems;   forming, at the at least one server, at least one rule or control algorithm usable by a window system, based on the information from the plurality of window systems and the information available on the network;   sending, from the at least one server to a window system of the plurality of window systems, the at least one rule or control algorithm; and   adjusting transmissivity of at least one of the plurality of windows of the window system, based on the at least one rule or control algorithm.   
     
     
         16 . The method of  claim 15 , further comprising:
 determining electricity usage of a building or portion of a building having the window system, based on information from an electric utility, wherein the at least one rule or control algorithm is based on the electricity usage and wherein the at least one rule or control algorithm limits transmissivity of at least a subset of the plurality of smart windows in the building or portion of the building.   
     
     
         17 . The method of  claim 15 , further comprising:
 co-operating the at least one server and at least one application on at least one user device to share a collection of rules or control algorithms applicable to window systems.   
     
     
         18 . The method of  claim 15 , further comprising:
 providing information from sensors of windows of the window system and further window systems to a weather forecasting service or server.   
     
     
         19 . The method of  claim 15 , further comprising:
 generating an environmental model for each of the plurality of windows of the window system, based on information from the at least one sensor of each of the plurality of windows and based on the information from the network including weather and daylight information, wherein the environmental model includes a shade model and wherein the at least one rule or control algorithm is based on the environmental model.   
     
     
         20 . The method of  claim 15 , further comprising:
 revising the at least one rule or control algorithm at the at least one server or at the window system, responsive to further information, wherein the smart window system with cloud learning is adaptive.