METHOD AND SYSTEM FOR CONTROLLING ENVIRONMENTAL CONDITIONS OF ENTITY

• US 20120245740A1
• Filed: 11/22/2010
• Published: 09/27/2012
• Est. Priority Date: 11/20/2009
• Status: Abandoned Application

• Current Assignee: Zerogroup Holding OUE
• Original Assignee: Zerogroup Holding OUE
• Inventors: Raestik, Raivo, Vimberg, Priit
• Application Number:
  US13/511,022
• Publication Number:
  US 20120245740A1
• 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 11/46 Improving electric energy efficiency or saving
• CPC Class Codes: F24F 11/62 characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• CPC Class Codes: G05B 13/027 using neural networks only

Abstract

An adjusting of environmental conditions of an entity, such as a room, is described, where the entity has desired environmental conditions to be maintained and/or achieved. The environmental conditions may relate to temperature, humidity, CO2 level, lighting, etc. The adjusting is implemented by equipments based on at least one controlling parameter provided by a controlling means. The controlling means is provided with at least one environmental condition measurement data related to the entity and measured by a measuring means. In addition the controlling means is provided with at least one outer paramenter, such as weather conditions information. The controlling parameter is generated by using a neural algorithm having at least the following input: at least one measured environmental condition parameter related to the entity; and at least one outer paramenter.

Claims

1. A method for adjusting environmental conditions of an entity, characterized in that, the entity has desired environmental condition to be maintained and/or achieved, and in that the method comprises

a) adjusting the environmental condition of the entity by equipments, where the adjusting is based on at least one controlling parameter provided by a controlling means,

b) measuring at least one environmental condition in said entity by a measuring means and signalling it to the controlling means,

c) signalling at least one outer parameter to the controlling means, where said outer parameter is independent of the entity'"'"'s property,

d) providing said at least one controlling parameter to said equipments by said controlling means, where the controlling parameter is generated by using a neural algorithm having at least one of the following as an input:

at least one measured environmental condition parameter related to said entity, and at least one outer parameter relating to outer information.

2. A method of claim 1, wherein said environmental condition relates to at least one of the following:

a) indoor temperatures,

b) indoor humidity,

c) indoor CO₂-level, and/or

d) indoor/outdoor lighting,

and wherein said controlling means controls at least one of the following equipment;

e) heating means,

f) cooling means,

g) ventilation means,

h) lighting means and/or

i) means for affecting humidity.

3. A method of claim 1, wherein the outer information relates to general and/or identified presence information of a user in the entity and/or predicted location information of the user indicating when the user will leave or arrive in the entity, where said predicted location information of the user is generated using a neural network, self-learning algorithms and/or traffic information gathered from the environment where the user moves.

4. A method of claim 1, wherein the outer information relates to current outdoor weather conditions, weather forecast information, and/or tariff of energy costs.

5. A method of claim 1, wherein said neural algorithm is a self-learning neural algorithm, and it is used for generating heating inertia information about the entity taking into account the measured environmental conditions of the entity as well as the outer parameters and consumed energy determined by said neural algorithm, when the environmental condition of said entity is adjusted.

6. A method of claim 5, 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 an entity and at least one of the outer information, when the neural algorithm is adapted to determine the control parameter signal to said equipments, such as heating, cooling and/or ventilation means,

b) current and desired indoor humidity and at least one of the outer information, when the neural algorithm is adapted to determine the control parameter signal to said equipments, such as means for affecting humidity and/or ventilation means,

c) current and desired indoor CO₂-level and at least one of the outer information, when the neural algorithm is adapted to determine the control parameter signal to said equipments, such as ventilation means, and/or

d) current and desired indoor/outdoor lighting and at least one of the outer information, when the neural algorithm is adapted to determine the control parameter signal to said equipments, such as lighting means.

7. A method of claims 1, wherein the controlling parameters provided by said neural algorithm 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.

16. A computer program product for adjusting environmental conditions of an entity, characterized in that, the entity has desired environmental condition to be maintained and/or achieved, and in that the computer program product is adapted to perform the steps of claim 1, when said computer program product is run on a computer.

8. A controlling system for adjusting environmental conditions of an entity, characterized in that, the entity has desired environmental condition to be maintained and/or achieved, and in that the system comprises

a) equipments for adjusting the environmental condition of an entity, where the adjusting is based on at least one controlling parameter provided by a controlling means,

b) measuring means for measuring at least one environmental condition in said entity and signalling the measured environmental condition information to the controlling means, and

c) means for signalling at least one outer parameter to the controlling means, where said outer parameter is independent of the entity'"'"'s property,

and where

d) said controlling means is adapted generate said at least one controlling parameter by using a neural algorithm having at least one of the following as an input:

at least one measured environmental condition parameter related to said entity, and at least one outer parameter.

9. A controlling system claim 8, wherein said environmental condition information relates to at least one of the following:

indoor temperatures, indoor humidity, indoor CO₂-level, and/or indoor/outdoor lighting.

10. A controlling system of claim 8, wherein the outer information relates to presence information of a user in the entity and/or predicted location information of the user indicating when the user will leave or arrive in the entity, where said predicted location information of the user is generated using a neural network, self-learning algorithms and/or traffic information gathered from the environment where the user moves.

11. A controlling system of claim 8, wherein the outer information relates in addition to current outdoor weather conditions, weather forecast information, and/or tariff of energy costs.

12. A controlling system of claim 8, wherein said neural algorithm is a self-learning neural algorithm, and it is used for generating heating inertia information about the entity taking into account the measured environmental conditions of the entity as well as the outer parameters and the consumed energy determined by said neural algorithm, when the environmental condition of said entity is adjusted.

13. A controlling system of claim 8claim 12, 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 and at least one outer information, when the neural algorithm is adapted to determine the control parameter signal to said equipments, such as heating, cooling and/or ventilation means,

b) current and desired indoor humidity and at least one outer information, when the neural algorithm is adapted to determine the control parameter signal to said equipments, such as means for affecting humidity and/or ventilation means,

c) current and desired indoor CO₂-level and at least one outer information, when the neural algorithm is adapted to determine the control parameter signal to said equipments, such as ventilation means, and/or

d) current and desired indoor/outdoor lighting and at least one outer information, when the neural algorithm is adapted to determine the control parameter signal to said equipments, such as lighting means.

14. A controlling system of claim 8, wherein the controlling parameters provided by said neural algorithm 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 controlling system of claim 8, wherein the controlling system is adapted to control at least two and preferably three different environmental conditions/magnitudes of an entity on the basis of the neural algorithm.