Method and system for controlling environmental conditions of different entities

• US 8,874,497 B2
• Filed: 11/22/2010
• Issued: 10/28/2014
• Est. Priority Date: 11/20/2009
• Status: Expired due to Fees

• Current Assignee: Zerogroup Holding OUE
• Original Assignee: Zerogroup Holding OUE
• Inventors: Raestik, Raivo, Vimberg, Priit
• Primary Examiner(s): Holmes, Michael B
• Application Number:
  US13/511,009
• Publication Number:
  US 20120239603A1
• Time in Patent Office: 1,436 Days
• US Class Current: 706/21
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 19/042 using digital processors G05B19/05 takes precedence
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house

Abstract

The invention relates to a controlling system for adjusting environmental conditions of at least one entity, wherein the entity has desired environmental conditions for at least two different states. The system comprises equipments controlled by controlling means for changing and/or maintaining the environmental condition of the entities. The controlling means is adapted to provide controlling parameters to equipments for adjusting the environmental condition of said entity so that at least one parameter used for controlling the environmental condition of said entity depends on at least one measured environmental condition parameter of another entity being different from the entity, which environmental condition is adjusted by said equipment.

Claims

1. A controlling system for adjusting environmental conditions of at least first and second entities, characterized in that, each of the entities has desired environmental conditions for at least two different states, and that the system comprises

a) first equipments for adjusting the environmental condition of said first entity, and second equipments for adjusting the environmental condition of said second entity, where the adjusting is based on controlling parameters,

b) first controlling means for controlling the function of said first equipments, and second controlling means for controlling the function of said second equipments,

c) first measuring means for measuring environmental condition in said first entity and signalling it to the first controlling means, and second measuring means for measuring environmental condition in said second entity and signalling it to the second controlling means, and

wherein

d) said first and second controlling means are adapted to provide said controlling parameters to said first and second equipments so that at least one parameter used for controlling the environmental condition of the first entity depends on at least one measured environmental condition parameter of the second entity, and that at least one parameter used for controlling the environmental condition of the second entity depends on at least one measured environmental condition parameter of the first entity, where said first entity is different that said second entity, wherein said first and second controlling means are provided with general or identified presence information of the user in at least one entity and/or predicted location information of the user indicating when the user will leave or arrive in the first and/or second entity.

2. A controlling system of claim 1, wherein the controlling means is adapted to signalling the equipment of its entity to go into the occupied or non-occupied state based on said presence information and/or predicted location information.

3. A controlling system of claim 1, wherein said predicted location information of the user is generated using a neural network, self-learning algorithms and/or traffic information gathered from the environment between the entities the user moves.

4. A controlling system of claim 1, wherein a self-learning neural algorithm is used for providing said controller with said predicted location information of the user, and/or wherein said self-learning neural algorithm is used for providing the controlling means with at least one of the following inputs:

a) heating inertia information about the entities determined by said neural algorithm,

b) current outdoor weather conditions,

c) weather forecast information, and/or

d) tariff of energy costs for changing the environmental conditions of the building.

5. A controlling system of claim 1, wherein said self-learning neural algorithm is adapted to take into account at least one of the following:

a) current and desired indoor temperatures (of the entity in question and/or also of the other nearest) and possibly at least one of the inputs 5.a)-5.d) of claim 4 when it is adapted to determine the control parameter signal to said heating, cooling and/or ventilation means,

b) current and desired indoor humidity and possibly at least one of the inputs 5.a)-5.d) of claim 4 when it is adapted to determine the control parameter signal to said means for affecting humidity and/or ventilation means,

c) current and desired indoor CO₂-level and possibly at least one of the inputs 5.a)-5.d) of claim 4 when it is adapted to determine the control parameter signal to said ventilation means, and/or

d) current and desired indoor/outdoor lighting and possibly at least one of the inputs 5.a)-5.d) of claim 4 when it is adapted to determine the control parameter signal to said lighting means.

6. A controlling system of claim 1, wherein the controlling parameters provided by said controlling means is based also on the measured environmental condition and said desired environmental condition for said entity, which environmental condition is to be adjusted, in order to achieve or maintain said desired environmental condition for the desired state of said entity.

8. A method of claim 1, wherein said first and second controlling means are provided with general or identified presence information of the user in at least one entity and/or predicted location information of the user indicating when the user will left or arrive in the first and/or second entity.

9. A method of claim 8, wherein the controlling means signals the equipment of its entity to go into the occupied or non-occupied state based on said presence information and/or predicted location information.

10. A method of claim 8, wherein said predicted location information of the user is generated using a neural network, self-learning algorithms and/or traffic information gathered from the environment between the entities the user moves.

11. A method of claim 8, wherein a prediction, such as preferably self-learning neural algorithm, is used for providing said controller with predicted location information of the user, and/or wherein said prediction is used for providing the controlling means with at least one of the following inputs:

a) heating inertia information about the entities determined by said neural algorithm,

b) current outdoor weather conditions,

c) weather forecast information, and/or

d) tariff of energy costs for changing the environmental conditions of the building.

12. A method of claim 8, wherein said self-learning neural algorithm is adapted to take into account at least one of the following:

a) current and desired indoor temperatures (of the entity in question and/or also of the other nearest) and possibly at least one of the inputs 5.a)-5.d) of claim 5 when it is adapted to determine the control parameter signal to said heating, cooling and/or ventilation means,

b) current and desired indoor humidity and possibly at least one of the inputs 5.a)-5.d) of claim 5 when it is adapted to determine the control parameter signal to said means for affecting humidity and/or ventilation means,

c) current and desired indoor CO₂-level and possibly at least one of the inputs 5.a)-5.d) of claim 5 when it is adapted to determine the control parameter signal to said ventilation means, and/or

d) current and desired indoor/outdoor lighting and possibly at least one of the inputs 5.a)-5.d) of claim 5 when it is adapted to determine the control parameter signal to said lighting means.

13. A method of claim 8, wherein the control parameters are provided to the equipment with a certain delay and in a certain time window.

14. A method of claim 8, wherein the controlling parameters provided by said controlling means is based also on the measured environmental condition and said desired environmental condition for said entity, which environmental condition is to be adjusted, in order to achieve or maintain said desired environmental condition for the desired state of said entity.

15. A computer program product for adjusting environmental conditions of at least first and second entities, characterized in that, each of the entities has desired environmental conditions for at least two different states, and in that the computer program product is adapted to perform the steps of claim 8, when said computer program product is run on a computer.

7. A controlling means for adjusting environmental conditions of at least one entity, wherein the entity has desired environmental conditions for at least two different states, characterized in that,

the controlling means is adapted to provide controlling parameters to equipments for adjusting the environmental condition of said entity so that at least one parameter used for controlling the environmental condition of said entity depends on at least one measured environmental condition parameter of another entity being different from the entity, which environmental condition is adjusted by said equipment, wherein the controlling means is provided with general or identified presence information of the user in at least one entity and/or predicted location information of the user indicating when the user will left or arrive in the entity which environmental condition is to be adjusted, whereupon the controlling means is adapted to signalling the equipment of its entity to go into the occupied or non-occupied state based on said presence information and/or predicted location information.