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US 9,078,299 B2Granted
Predictive daylight harvesting system
AssigneeSuntracker Technologies Ltd.
InventorIan Ashdown
Life of this patent
Estimating the term…
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.
Claims8
A method performed by a predictive daylight harvesting system, the method comprising:
- receiving data values regarding a plurality of variable building lighting design parameters, including luminaire locations and illumination levels;
- calculating effects on a building's environmental characteristics based on the data values regarding the plurality of variable building lighting design parameters;
- changing at least one of the data values regarding the plurality of variable building lighting design parameters;
- selecting building lighting design parameter settings for the variable building lighting design parameters to maximize energy savings while maintaining selected occupant requirements for the building's environmental characteristics, including at least selected occupant requirements for lighting;
- measuring actual solar insolation and fine-tuning the selected building lighting design parameter settings to maximize energy savings while maintaining the selected occupant requirements for lighting;
- 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 the selected occupant requirements for lighting;
- analyzing occupant behavior, based on input from occupancy event sensors and personal lighting controls actions, and further determining the optimal strategy for predictive daylight harvesting that maximizes energy savings while maintaining the selected occupant requirements for lighting based on predicted occupant behavior;
- and
- interacting with a building's lighting control system and implementing the optimal strategy for maximizing energy savings while maintaining the selected occupant requirements for lighting.
6 more elements
A predictive daylight harvesting system, comprising:
- a controller that:
- reads input data from a plurality of sensors and information feeds, the sensors and information feeds including daylight photosensors, occupancy sensors, timers, personal lighting controls, weather report feeds, and energy storage controllers;
- calculates effects of variable building design parameters on a building's environmental characteristics, including at least selected occupant requirements for lighting;
- outputs controller command signals in order to maximize energy savings while maintaining selected occupant requirements for the building's environmental characteristics;
- and
- includes virtual representations of the building's exterior and interior environments, including geometry and material properties of objects that influence the distribution of daylight and artificial luminous flux within the exterior and interior environments, which virtual representations of the building's exterior and interior environments are accessed by the controller to perform calculations on effects of solar insolation on building energy management;
- and
- in which the controller receives and processes information about luminaires located in the building's interior environment, including photometric and electrical properties of the luminaires, and receives information from daylight photosensors and occupancy sensors located in the building's interior environment, and further comprising:
- virtual representations of occupants and their behaviors, including where the occupants are likely to be located within the building's interior environment at any given time and date, and the occupants' personal lighting preferences, which the virtual representations of the occupants and their behaviors are accessed by the controller for the purpose of calculating optimal output settings for the controller command signals in order to maximize energy savings while maintaining selected occupant requirements for lighting;
- and
- 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 enerqy savings while providing an interior luminous environment that is consistent with predefined goals and occupant preferences.
9 more elements
Dependencies are inferred from the claim text.