Building management system having set offset value learning and set bias value determining system for controlling thermal environment

• US 5,751,916 A
• Filed: 03/27/1996
• Issued: 05/12/1998
• Est. Priority Date: 05/03/1994
• Status: Expired due to Fees

• Current Assignee: Yamatake Corporation
• Original Assignee: Yamatake-Honeywell Company Limited
• Inventors: Kon, Akihiko, Naruse, Akihiko
• Primary Examiner(s): Lim, Krisna
• Application Number:
  US08/622,923
• Time in Patent Office: 776 Days
• Field of Search: 395/77, 395/3, 364/550
• US Class Current: 706/2
• 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 11/523 for displaying temperature data
• 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: F24F 11/64 using pre-stored data
• CPC Class Codes: F24F 2120/20 Feedback from users
• CPC Class Codes: G05B 13/0285 using neural networks and fuzzy logic
• CPC Class Codes: G05D 22/02 characterised by the use of electric means
• CPC Class Codes: G05D 23/1917 using digital means
• CPC Class Codes: G06N 3/043 based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
• CPC Class Codes: G06N 3/045 Combinations of networks

Abstract

A building management set value decision support apparatus includes a thermal environment index calculation/display section, and a pseudo-thermal environment index calculation/display section. The thermal environment index calculation/display section calculates/displays the current value of a thermal environment index on the basis of the measured value of an air-conditioning control target and the measured or preset values of other predetermined parameters. The pseudo-thermal environment index calculation/display section calculates/displays a pseudo-thermal environment index on the basis of a supplied building management set value and the measured or preset values of the other predetermined parameters. A set value learning apparatus, a set value determining apparatus, and a neural network operation apparatus are also disclosed.

Claims

1. A set offset value learning and set bias value determining apparatus comprising:

a set offset value learning apparatus comprising;

a neural network having an input layer, an intermediate layer, and an output layer;

an intermediate layer unit generation means for generating intermediate layer units in said intermediate layer in response to a command of changing an air-conditioning set value, set with respect to an air-conditioning control target, to a tenant set value;

a set offset value storage means for causing said intermediate layer unit generated by said intermediate layer unit generation means to store a difference between a management set value, set with respect to said air-conditioning control target, and said tenant set value as a set offset value;

an intrinsic number storage means for causing said intermediate layer unit generated by said intermediate layer unit generation means to store an intrinsic number indicating an ordinal number in a sequence of generation; and

a membership function storage means for causing said intermediate layer unit generated by said intermediate layer unit generation means to respectively determine and store membership functions on the basis of a measured value of said air-conditioning control target, obtained when said intermediate layer unit is generated, and a measured or preset value of other predetermined parameters; and

a set bias value determining apparatus comprising;

a re-triggerable counter having a count value being cleared to zero in accordance with a command of changing said air-conditioning set value to said tenant set value;

an activation amount determination means for determining an activation amount of each said intermediate layer unit in response to an incrementing operation of said re-triggerable counter by performing a fuzzy operation between each of said membership functions stored in said intermediate layer unit and a corresponding one of said membership functions of said measured value of said air-conditioning control target, obtained at the time of said incrementing operation, and said preset value of said other predetermined parameters;

an information pack output means for outputting an information pack representing said activation amount, said set offset value, and said intrinsic number, said set offset value and said intrinsic number being stored in said intermediate layer unit;

an air-conditioning set value calculation means for selecting said set offset value of said intermediate layer unit having a maximum activation amount which has a maximum intrinsic number based on said information pack, adding said selected set offset value, as a set bias value, to said management set value, and setting a sum as a new air-conditioning set value in place of said tenant set value;

an air-conditioning set value forcible change means for forcibly changing said air-conditioning set value to said tenant set value in accordance with a change command to said tenant set value of said air-conditioning set value; and

a tenant temperature preference setting and learning means for obtaining a temperature setting preferred by a tenant, said temperature setting entered into a tenant user terminal.

2. An apparatus according to claim 1, wherein said intermediate layer unit generation means generates a plurality of intermediate units said apparatus further comprising means for reusing a first intermediate layer unit having a minimum intrinsic number stored at a time point as a second intermediate layer unit is generated, when the number of said intermediate layer units generated reaches a maximum number of said intermediate layer units which can be generated.

3. A set offset value learning and set bias value determining apparatus comprising:

a set offset value learning apparatus comprising;

a neural network having an input layer, an intermediate layer, and an output layer;

an intermediate layer unit generation means for generating an intermediate layer unit in said intermediate layer in response to a command of changing an air-conditioning set value, set with respect to an air-conditioning control target, to a tenant set value;

a set offset value storage means for causing said intermediate layer unit generated by said intermediate layer unit generation means to store a difference between a management set value, set with respect to said air-conditioning control target, and said tenant set value as a set offset value;

an intrinsic number storage means for causing said intermediate layer unit generated by said intermediate layer unit generation means to store an intrinsic number indicating an ordinal number in a sequence of generation; and

a membership function storage means for causing said intermediate layer unit generated by said intermediate layer unit generation means to respectively determine and store membership functions on the basis of a measured value of said air-conditioning control target, obtained when said intermediate layer unit is generated, and a measured or preset value of other predetermined parameters; and

a set bias value determining apparatus comprising;

an activation amount determination means for determining an activation amount of each said intermediate layer unit in response to a command of changing said management set value by performing a fuzz operation between each of said membership functions stored in said intermediate layer unit and a corresponding one of said membership functions of a said measured value of said air-conditioning control target, obtained when said command is issued, and said preset value of said other predetermined parameters;

an information pack output means for outputting an information pack representing said activation amount, said set offset value, and said intrinsic number, said set offset value and said intrinsic number being stored in said intermediate layer unit;

an air-conditioning set value calculation means for selecting said set offset value of said intermediate layer unit with a maximum activation amount, or said set offset value of one of a plurality of said intermediate layer units each having a maximum said activation amount which has a maximum said intrinsic number based on said information pack output, adding said selected set offset value, as a set bias value, to a value obtained by changing said management set value to obtain a sum, and setting said sum as a new air-conditioning set value; and

a tenant temperature preference setting and learning means for obtaining a temperature setting preferred by a tenant, said temperature setting entered into a tenant user terminal.

4. An apparatus according to claim 3, further comprising means for reusing a first intermediate layer unit, when the number of said intermediate layer units generated reaches a maximum number of said intermediate layer units which can be generated where a minimum said intrinsic number is stored at a time point, as a said intermediate layer unit to be subsequently generated.

5. A set offset value learning and set bias value determining apparatus comprising:

a set offset value learning apparatus comprising;

a neural network having an input layer, an intermediate layer, and an output layer;

an intermediate layer unit generation means for generating an intermediate layer unit in said intermediate layer in response to a command of changing an air-conditioning set value, set with respect to an air-conditioning control target, to a tenant set value;

a set offset value storage means for causing said intermediate layer unit generated by said intermediate layer unit generation means to store a difference between a management set value, set with respect to said air-conditioning control target, and said tenant set value as a set offset value; and

a membership function storage means for causing said intermediate layer unit generated by said intermediate layer unit generation means to respectively determine and store membership functions on the basis of a measured value of said air-conditioning control target, obtained when said intermediate layer unit is generated, and the measured or preset values of other predetermined parameters; and

a set bias value determining apparatus comprising;

a re-triggerable counter having a count value being cleared to zero in accordance with a command of changing said air-conditioning set value to said tenant set value;

an activation amount determination means for determining an activation amount of each said intermediate layer unit in response to an incrementing operation of said re-triggerable counter by performing a fuzzy operation between each of said membership functions stored in said intermediate layer unit and a corresponding one of said membership functions of said measured value of said air-conditioning control target, obtained at the time of said incrementing operation, and said preset values of said other predetermined parameters;

an information pack output means for outputting an information pack representing at least said activation amount and said set offset value stored in said intermediate layer unit;

an air-conditioning set value calculation means for obtaining a set bias value by dividing a sum total of products of said activation amounts and said set offset values of the respective information packs output from said information pack output means by a sum total of said activation amounts of said information packs, adding said set bias value to said management set value, and setting a sum as a new air-conditioning set value in place of said tenant set value;

an air-conditioning set value forcible change means for forcibly changing said air-conditioning set value to said tenant set value in accordance with a change command to said tenant set value of said air-conditioning set value; and

a tenant temperature preference setting and learning means for obtaining a temperature setting preferred by a tenant, said temperature setting entered into a tenant user terminal.

6. A set offset value learning and set bias value determining apparatus comprising:

a set offset value learning apparatus comprising;

a neural network having an input layer, an intermediate layer, and an output layer;

an intermediate layer unit generation means for generating an intermediate layer unit in said intermediate layer in response to a command of changing an air-conditioning set value, set with respect to an air-conditioning control target, to a tenant set value;

a set offset value storage means for causing said intermediate layer unit generated by said intermediate layer unit generation means to store a difference between a management set value, set with respect to said air-conditioning control target, and said tenant set value as a set offset value; and

a membership function storage means for causing said intermediate layer unit generated by said intermediate layer unit generation means to respectively determine and store membership functions on the basis of a measured value of said air-conditioning control target, obtained when said intermediate layer unit is generated, and the measured or preset values of other predetermined parameters; and

a set bias value determining apparatus comprising;

an activation amount determination means for determining an activation amount of each said intermediate layer unit in response to a command of changing said management set value by performing a fuzzy operation between each of said membership functions stored in said intermediate layer unit and a corresponding one of said membership functions of said measured value of said air-conditioning control target, obtained when said command is issued, and said preset values of said other predetermined parameters;

an information pack output means for outputting an information pack representing at least said activation amount and said set offset value stored in said intermediate layer unit;

an air-conditioning set value determination means for obtaining a set bias value by dividing a sum total of products of said activation amounts and said set offset values of the respective information packs output from said information pack output means by a sum total of said activation amounts of said information packs, adding said set bias value to a value obtained by changing said management set value, and setting a new air-conditioning set value; and

a tenant temperature preference setting and learning means for obtaining a temperature setting preferred by a tenant, said temperature setting entered into a tenant user terminal.

7. A neural network operation apparatus comprising:

first to Nth input layer units for receiving first to Nth parameters, at least one of which serves as a measured value, wherein said first to Nth input layer units send said first to Nth parameters as a set of fuzzy values every time data indicating dissatisfaction with the air conditioning environment is present;

intermediate layer units arranged in correspondence with a set of lattice points of a feature space formed by a set of axes of said first to Nth parameters of said first to Nth input layer units, each unit of said intermediate layer units having a membership function centered on a corresponding lattice point of said set of lattice points for each axis of said set of axes each said membership function having a region overlapping a membership function centered on an adjacent lattice point, and said intermediate layer units receiving outputs from said first to Nth input layer units each intermediate layer unit of said intermediate layer units determines an activation amount by performing a fuzzy operation between each of said membership functions held therein and a corresponding one of said membership functions of said first to Nth parameters sent as said fuzzy values from said first to Nth input layer units, and supplies said activation amount to an output layer unit;

said output layer unit for receiving outputs from said intermediate layer units said output layer unit determines a weighting coefficient for each intermediate layer unit of said intermediate layer units, which has supplied said activation amount, on the basis of said activation amount supplied from each said intermediate layer unit and an offset value of a thermal environment index, and sends said weighting coefficient to a corresponding intermediate layer unit of said intermediate layer units;

said intermediate layer unit stores and learns said weighting coefficient sent from said output layer unit as a new weighting coefficient, or stores and learns a weighting coefficient obtained by a calculation based on said weighting coefficient sent from said output layer unit and a stored weighting coefficient as said new weighting coefficient; and

a tenant temperature preference setting and learning means for obtaining a temperature setting preferred by a tenant, said temperature setting entered into a tenant user terminal.

8. A neural network operation apparatus according to claim 7 further comprising

a back propagation learning type neural network comprising a set of input layer units to which first to Nth parameters are branched and input, a set of intermediate layer units for receiving outputs from said input layer units, and an output layer unit for receiving outputs from said set of intermediate layer units and for outputting a standard measured value of a thermal environment index.

9. A neural network operation apparatus according to claim 7 further comprising:

a back propagation learning type neural network comprising a set of input layer units to which first to Nth parameters are branched and input, a set of intermediate layer units for receiving outputs from said set of input layer units, and an output layer unit for receiving outputs from said set of intermediate layer units and for outputting a standard measured value of a predicted mean vote (PMV) as a thermal environment index;

a first PMV inverse operation means for receiving said first to Nth parameters and a sum of a PMV bias value of said predicted mean vote from said neural network operation apparatus outside of said back propagation learning type neural network and said standard measured value of said predicted mean vote from said back propagation learning type neural network, converting said first to Nth parameters and said sum into an effective temperature by a first PMV inverse operation, and outputting said effective temperature; and

a second PMV inverse operation means for receiving said first to Nth parameters and a set value of said predicted mean vote, converting said first to Nth parameters and said set value into a set effective temperature by a second PMV inverse operation, and outputting said set effective temperature.

10. A neural network operation apparatus comprising:

first to Nth input layer units for receiving first to Nth parameters at least one of which serves as a measured value, said first to Nth input layer units send said first to Nth parameters as fuzzy values every time data indicating dissatisfaction with the air-conditioning environment is present;

intermediate layer units arranged in correspondence with a set of lattice points of a feature surface formed by a set of axes of said first to Nth parameters of said first to Nth input layer units, each unit of said intermediate layer units having a membership function centered on a corresponding lattice point of said set of lattice points for each axis of said set of axes each said membership function having a region overlapping a membership function centered on an adjacent lattice point and said intermediate layer units receiving outputs from said first to Nth input layer units;

an output layer unit for receiving outputs from said intermediate layer units said output layer unit determines a weighting coefficient for each said intermediate layer unit, which has supplied said activation amount, on the basis of the activation amount supplied from said intermediate layer unit and an offset value of a thermal environment index, and sends said weighting coefficient to a corresponding intermediate layer unit of said intermediate layer units;

each intermediate layer unit of said intermediate layer units determines an activation amount by performing a fuzzy operation between each membership function of said membership functions held therein and a corresponding one of said membership functions of said first to Nth parameters sent as said fuzzy values from said first to Nth input layer units, and supplies said activation amount to said output layer unit;

said corresponding intermediate layer unit stores and learns said weighting coefficient sent from said output layer unit as a new weighting coefficient, or stores and learns a weighting coefficient obtained by a calculation based on said weighting coefficient sent from said output layer unit and a stored weighting coefficient as a new weighting coefficient;

in a normal operation, said first to Nth input layer units respectively send said input first to Nth parameters, as said fuzzy values, to said intermediate layer units;

each said intermediate layer unit determines said activation amount by performing said fuzzy operation between each of said membership functions held therein and a corresponding one of membership functions of said first to Nth parameters sent as said fuzzy values from said first to Nth input layer units, and supplies said activation amount and said stored weighting coefficient to said output layer unit;

said output layer unit determines a sum total of products of said activation amounts and said weighting coefficient sent from said intermediate layer units, and determines and outputs said sum total as a bias value of said thermal environment index; and

a tenant temperature preference setting and learning means for obtaining a temperature setting preferred by a tenant, said temperature setting entered into a tenant user terminal.

11. A neural network operation apparatus according to claim 10 further comprising

a back propagation learning type neural network comprising a set of input layer units to which first to Nth parameters are branched and input, a set of intermediate layer units for receiving outputs from said input layer units, and an output layer unit for receiving outputs from said set of intermediate layer units and for outputting a standard measured value of a thermal environment index.

12. A neural network operation apparatus according to claim 10 further comprising:

a back propagation learning type neural network comprising a set of input layer units to which first to Nth parameters are branched and input, a set of intermediate layer units for receiving outputs from said set of input layer units, and an output layer unit for receiving outputs from said set of intermediate layer units and for outputting a standard measured value of a predicted mean vote (PMV) as a thermal environment index;

a first PMV inverse operation means for receiving said standard measured value of said predicted mean vote from said back propagation learning type neural network and said first to Nth parameters, converting said standard measured value and said first to Nth parameters into an effective temperature by a first PMV inverse operation, and outputting said effective temperature; and

a second PMV inverse operation means for receiving said first to Nth parameters and a difference between a bias value of said predicted mean vote from said neural network apparatus outside of said back propagation learning type neural network and a set value of said predicted mean vote, converting the difference and said first to Nth parameters into a set effective temperature, and outputting said set effective temperature.

13. A neural network apparatus comprising

first to Nth input layer units for receiving first to Nth parameters at least one of which serves as a measured value;

intermediate layer units arranged in correspondence with a set of lattice points of a feature space formed by a set of axes of said first to Nth parameters of said first to Nth input layer units, each unit of said intermediate layer units having a membership function centered on a corresponding lattice point of said set of lattice points for each axis of said set of axes, each said membership function having a region overlapping a membership function centered on an adjacent lattice point and said intermediate layer units receiving outputs from said first to Nth input layer units,

an output layer unit for receiving outputs from said intermediate layer units

a tenant temperature preference setting and learning means for obtaining a temperature setting preferred by a tenant, said temperature setting entered into a tenant user terminal;

a back propagation learning type neural network comprising;

a set of input layer units to which first to Nth parameters are branched and input;

a set of intermediate layer units for receiving outputs from said input layer units; and

an output layer unit for receiving outputs from said set of intermediate layer units and for outputting a standard measured value of a thermal environment index.

14. A neural network operation apparatus comprising:

first to Nth input layer units for receiving first to Nth parameters at least one of which serves as a measured value;

intermediate layer units arranged in correspondence with a set of lattice points of a feature space formed by a set of axes of said first to Nth parameters of said first to Nth input layer units, each unit of said intermediate layer units having a membership function centered on a corresponding lattice point of said set of lattice points for each axis of said set of axes, each said membership function having a region overlapping a membership function centered on an adjacent lattice point, and said intermediate layer units receiving outputs from said first to Nth input layer units;

an output layer unit for receiving outputs from said intermediate layer units;

a tenant temperature preference setting and learning means for obtaining a temperature setting preferred by a tenant, said temperature setting entered into a tenant user terminal;

a back propagation learning type neural network comprising;

a set of input layer units to which first to Nth parameters are branched and input;

a set of intermediate layer units for receiving outputs from said input layer units; and

an output layer unit for receiving outputs from said set of intermediate layer units and for outputting a standard measured value of a predicted mean vote (PMV) as a thermal environment index;

wherein a PMV bias value of said predicted mean vote (PMV) is obtained as a bias value of a thermal environment index from said neural network operation apparatus outside of said back propagation learning type neural network, said PMV bias value is added to said predicted mean vote from said back propagation learning type neural network, a sum is converted into an effective temperature by a PMV inverse operation, and said effective temperature is an output.