PREDICTIVE DAYLIGHT HARVESTING SYSTEM

• US 20120265350A1
• Filed: 04/13/2012
• Published: 10/18/2012
• Est. Priority Date: 04/14/2011
• Status: Active Grant

• Current Assignee: SunTracker Technologies Ltd.
• Original Assignee: SunTracker Technologies Ltd.
• Inventors: ASHDOWN, IAN
• Granted Patent: US 9,078,299 B2
• Time in Patent Office: Days
• US Class Current: 700/276
• CPC Class Codes: F24F 11/30 for purposes related to the operation of the system, e.g. for safety or monitoring
• CPC Class Codes: F24F 11/32 Responding to malfunctions or emergencies
• CPC Class Codes: F24F 11/47 Responding to energy costs
• CPC Class Codes: F24F 2120/10 Occupancy
• CPC Class Codes: F24F 2130/20 Sunlight
• CPC Class Codes: F24S 2201/00 Prediction; Simulation
• CPC Class Codes: H05B 47/105 in response to determined parameters
• CPC Class Codes: H05B 47/11 by determining the brightness or colour temperature of ambient light
• CPC Class Codes: H05B 47/115 by determining the presence or movement of objects or living beings
• CPC Class Codes: H05B 47/16 by timing means
• CPC Class Codes: Y02B 20/40 Control techniques providing energy savings, e.g. smart controller or presence detection
• CPC Class Codes: Y02E 10/40 Solar thermal energy, e.g. solar towers

Abstract

In the context of a predictive daylight harvesting system data values are input regarding a plurality of variable building design parameters. The effects on a building'"'"'s environmental characteristics are calculated based on the data values regarding a plurality of building design parameters. At least one of the data values is changed regarding variable building design parameters. The effects on a building'"'"'s environmental characteristics are recalculated based on the data values regarding a plurality of building design parameters building heat balance.

Claims

1. A predictive daylight harvesting system, comprising the steps of

a) inputting data values regarding a plurality of variable building design parameters;

b) calculating the effects on a building'"'"'s environmental characteristics based on the data values regarding a plurality of building design parameters;

c) changing at least one of the data values regarding variable building design parameters;

d) recalculating the effects on a building'"'"'s environmental characteristics based on the data values regarding a plurality of building design parameters building heat balance.

2. The predictive daylight harvesting system of claim 1, comprising the further steps of:

selecting building design parameter settings to maximize energy savings while maintaining selected occupant requirements for a building'"'"'s environmental characteristics; and

measuring actual solar insolation and fine-tuning selected building design parameter settings to maximize energy savings while maintaining selected occupant requirements for heating and lighting.

3. The predictive daylight harvesting system of claim 1, comprising the further steps of analyzing a combination of historical weather data, in situ daylight measurements over time, and current weather predictions, and determining an optimal strategy for predictive daylight harvesting that maximizes energy savings while maintaining selected occupant requirements for heating and lighting.

4. The predictive daylight harvesting system of claim 1, comprising the further steps of analyzing occupant behaviour, based on input from occupancy event sensors and personal lighting controls actions, and determining an optimal strategy for daylight harvesting that maximizes energy savings while maintaining selected occupant requirements for heating and lighting, based on predicted occupant behaviour.

5. The predictive daylight harvesting system of claim 1, comprising the further steps of interacting with a building'"'"'s HVAC control system and implementing an optimal strategy for maximizing energy savings while maintaining selected occupant requirements for a building'"'"'s environmental characteristics;

in which the variable building design parameters include artificial light locations and illumination levels.

6. The predictive daylight harvesting system of claim 1, in which the variable building design parameters include thermal emissivity and thermal mass of objects and surfaces in a building'"'"'s interior environment for the purpose of determining radiative heat transfer within the environment due to solar insolation.

7. The predictive daylight harvesting system of claim 2, comprising the further steps of:

a) measuring actual solar insolation and fine-tuning selected building design parameter settings to maximize energy savings while maintaining selected occupant requirements for heating and lighting;

b) analyzing a combination of historical weather data, in situ daylight measurements over time, and current weather predictions, and determining an optimal strategy for predictive daylight harvesting that maximizes energy savings while maintaining selected occupant requirements for heating and lighting;

c) analyzing occupant behaviour, based on input from occupancy event sensors and personal lighting controls actions, and determining an optimal strategy for daylight harvesting that maximizes energy savings while maintaining selected occupant requirements for heating and lighting, based on predicted occupant behaviour;

d) interacting with a building'"'"'s HVAC control system and implementing an optimal strategy for maximizing energy savings while maintaining selected occupant requirements for heating and lighting;

in which the variable building design parameters include artificial light locations and illumination levels, thermal emissivity and thermal mass of objects and surfaces in a building'"'"'s interior environment for the purpose of determining radiative heat transfer within the environment due to solar insolation.

8. A predictive daylight harvesting system comprising the steps of:

a) receiving input data from at least one ambient condition sensor and at least one information feed about anticipated solar conditions;

b) calculating a luminous environment for a building based on the input data;

c) generating messages based on output data about the calculated luminous environment;

d) transmitting the messages via an interconnect system to a building environmental control subsystem;

in order to maximize energy savings while maintaining selected occupant requirements for a building'"'"'s environmental characteristics.

9. A predictive daylight harvesting system, comprising a controller that:

a) reads input data from a variety of sensors and information feeds, the sensors and feeds to include at least a plurality of sensors and information feeds from among the class of sensors and information feeds that includes daylight photosensors, occupancy sensors, timers, personal lighting controls, utility power meters, weather report feeds, HVAC and energy storage controllers

b) calculates the effects of variable building design parameters, on a building'"'"'s environmental characteristics, and

c) outputs building design parameter setting command signals in order to maximize energy savings while maintaining selected occupant requirements for a building'"'"'s environmental characteristics.

10. The predictive daylight harvesting system of claim 9, further comprising virtual representations of building exterior and interior environments, including geometry and material properties of objects that influence the distribution of daylight and artificial luminous flux within the environments, which virtual representations of building exterior and interior environments are accessed by the controller to perform calculations on the effects of solar insolation on building heat balance and energy management;

and

in which the controller receives and processes information about luminaires located in a building'"'"'s interior environment, including photometric and electrical properties of the luminaires, and receives information from daylight photosensors and occupancy sensors located in the interior environment.

11. The predictive daylight harvesting system of claim 9, further comprising virtual representations of thermal emissivity and thermal mass of objects and surfaces in a building'"'"'s interior environment, which virtual representations of thermal emissivity and thermal mass of objects and surfaces in a building'"'"'s interior environment and accessed by the controller for the purpose of determining radiative heat transfer within the environment due to solar insolation.

12. The predictive daylight harvesting system of claim 9, further comprising virtual representations of occupants and their behaviours, including where the occupants are likely to be located within a building'"'"'s interior environments at any given time and date, and the occupants'"'"' personal lighting preferences, which virtual representations of occupants and their behaviours are accessed by the controller for the purpose of calculating optimal output setting for building design parameter setting command signals in order to maximize energy savings while maintaining selected occupant requirements for heating and lighting.

13. The predictive daylight harvesting system of claim 9, in which the controller includes a fuzzy-logic inference engine that learns weather patterns and occupant usage patterns and preferences.

14. The predictive daylight harvesting system of claim 9, in which the controller maintains a mathematical model of sky conditions, historical weather data, and a database of weather patterns and occupant usage patterns and preferences;

and in which the controller provides output data and command signals to luminaire dimmers and to automated fenestration devices.

15. The predictive daylight harvesting system of claim 9, in which the controller communicates with building HVAC systems.

16. The predictive daylight harvesting system of claim 9, in which the controller continually reads data from external input and communication devices, calculates the optimal settings for luminaires and fenestration devices, and controls luminaires and fenestration devices to achieve maximal energy savings while providing an interior luminous environment that is consistent with predefined goals and occupant preferences.

17. The predictive daylight harvesting system of claim 9, in which the controller assigns a unique identifier to finite elements in a building exterior and interior environments, and assigns spectral radiance values for the finite elements based on pixels of luminance projection.

18. The predictive daylight harvesting system of claim 9, further comprising multiple daylight photosensors, in which the controller combines outputs from the photosensors to learn during a training period which weighting of outputs from different photosensors more reliably predicts sky conditions.

19. The predictive daylight harvesting system of claim 10, in which the controller receives and processes information about luminaires located in a building'"'"'s interior environment, including photometric and electrical properties of the luminaires, and receives information from daylight photosensors and occupancy sensors located in the interior environment, and further comprising:

a) virtual representations of thermal emissivity and thermal mass of objects and surfaces in a building'"'"'s interior environment, which virtual representations of thermal emissivity and thermal mass of objects and surfaces in a building'"'"'s interior environment and accessed by the controller for the purpose of determining radiative heat transfer within the environment due to solar insolation;

b) virtual representations of occupants and their behaviours, including where the occupants are likely to be located within a building'"'"'s interior environments at any given time and date, and the occupants'"'"' personal lighting preferences, which virtual representations of occupants and their behaviours are accessed by the controller for the purpose of calculating optimal output setting for building design parameter setting command signals in order to maximize energy savings while maintaining selected occupant requirements for heating and lighting;

c) a fuzzy-logic inference engine that learns weather patterns and occupant usage patterns and preferences;

in which the controller maintains a mathematical model of sky conditions, historical weather data, and a database of weather patterns and occupant usage patterns and preferences, continually reads data from external input and communication devices, calculates the optimal settings for luminaires and fenestration devices, and controls luminaires and fenestration devices to achieve maximal energy savings while providing an interior luminous environment that is consistent with predefined goals and occupant preferences.

20. The predictive daylight harvesting system of claim 9, comprising:

a) at least one controller that reads input data from a variety of sensors and information feeds, and that includes an artificial intelligence engine;

b) at least one ambient condition sensor and at least one information feed;

c) an interconnect system operatively coupling the controller to the sensor and the information feed;

configured to provide output data suitable for dimming or switching luminaires and operating automated fenestration devices.