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Adaptive fuzzy control applied to home heating system
Fuzzy Sets and Systems 61 (1994) 29-35 North-Holland
29
Adaptive fuzzy control applied to home heating system C. v. Altrock Inform Software Corp., Evanston, IL 60201, USA
H.-O. Arend Viessmann Werke GmbH & Co., D-3559 Allendorf (Eder), Germany
B. Krause, C. Steffens and E. BehrensR6mmler INFORM GmbH, D-52076 Aachen, Germany Received May 1993 Revised August 1993
Abstract: To maximize both heating economy and comfort of a private home heating system, fuzzy-logic control has been used by a German company in a new generation of furnace controllers. The fuzzy-logic controller ensures optimal adaptation to changing customer heating demands while using one sensor less than the former generation. Both the fuzzy-logic controller and the conventional control system were implemented on a standard 8-bit microcontroller. The design, optimization and implementation of the fuzzy controller was supported by the software development system fuzzyTECH.
Keywords: Fuzzy logic control; customer habit adaptation; advanced tools; online optimization; embedded control; furnace control; climate control.
1. Introduction Most European houses have a centralized heating system which uses a furnace for diesel-type fuel to heat the water supply (boiler). From the boiler, the hot water is distributed by a pipe system to individual radiators in the rooms of the house. To meet the different needs of customer heating habits, the temperature of the furnace-heated water must constantly be adjusted in relation to the outdoor temperature (heat characteristic). To measure the outdoor Correspondence to: B. Krause, Inform GmbH, Pascalstr. 23, D-52076 Aachen, Germany.
temperature, a sensor is installed at the outside of the house. Figure 1 depicts the basic structure of such a system. The basic structure of a controller for this system is shown in Figure 2. The controller itself realizes a on-off characteristic. If the water temperature in the furnace drops to 2 Kelvin below the set temperature, the fuel valve opens and the ignition system starts the burning process. When the water temperature in the boiler itself rises to 2 Kelvin above the set temperature, the fuel valve closes. This on-off control strategy involving hysteresis minimizes the number of starts while assuring that the boiler temperature remains within the desired tolerance in contrast to a on-off control strategy without hysteresis. Although the structure of this control loop is quite simple, the task of determining the appropriate set boiler temperature is not. Since the maximum heat dissipation of the room radiators depends on the temperature of the incoming water (approximately the boiler temperature), the set point for the water temperature in the boiler must never be set so low that it cannot warm the house when necessary. On the other hand, an excessively high setting of the boiler temperature would result in energy loss in both the furnace and the piping system. Thus the set boiler temperature needs to be carefully set to ensure both user comfort and energy efficiency. In the 1950's, the German Electrical Engineering Society (VDE) defined the following procedure for this: based on the assumption that the maximum amount of heat, required by the house, depends on the outdoor temperature (Toutdoor), a parameterized function Tsetboiler = f(T,,utd,,or) was defined to adjust the set boiler temperature in relation to the outside temperature (heat characteristic) [3]. Parameters are the insulation coefficient of the house and a so-called 'comfort parameter'. The physical model of this
0165-0114/94/$07.00 ~ 1994---Elsevier Science B.V. All rights reserved SSDI: 0165-0114(93)E0181-Q
30
C. Altrock et al./Adaptive fuzzy control applied to home heating system
J
Outdoor Temperature
Fuel
=~
Circu~Pump
Furnace
Individual Radiators for Every Room
Fig. 1. Schemeof a centralized heating system. ,=
Toutdoor
:
: i i
!
:
1,
boiler
IF heat cl~arac'teHS'tic
I
J
IreCt
J T
[
i;;
Fig. 2. Blockschemeof the conventionalfurnacecontroller. is one in which the maximum amount of available heat equals the amount of heat disposed by the house plus some excess energy to compensate occasional door and window opening. Back in those days when most houses had only poor thermal insulation, the assumption, that the energy to be delivered by a heating system was largely outdoor-temperature dependent, was appropriate. Today, this is obsolete. Due to rising energy costs and environmental concerns, modern houses are built with improved insulation. Therefore, to achieve high efficiency, the outdoor temperature is not the only parameter which reflects the required energy amount. Other factors, such as ventilation, door/window openings and personal lifestyle, have to be considered as well.
2. The fuzzy controller Basically, there are two approaches for determining the appropriate set boiler temperature for a well-insulated house: • extensive use of sensors (i.e. temperature
sensors in every room) and use of a mathematical model. • definition of engineering heuristics to determine the set boiler temperature; based on a knowledge-based evaluation of existing sensor data. Since the use of extensive sensors is expensive and the construction of a comprising mathematical model is of overwhelming complexity, the second approach has been chosen for realizing the new generation of heating system controllers. The most important criteria about individual customer heat demand patterns comes from the actual energy consumption curve of the house, which is measured by the on/off-ratio of the burner. An example of such a curve is given in Figure 3. From this curve, four describing parameters are derived: (1) Current energy consumption, indicating current load. (2) Medium term tendency (I), indicating heating-up and heating-down phases. (3) Short term tendency (II), indicating disturbances like door/window openings.
C. Ahrock et al./Adaptive fuzzy control applied to home heating system
(4) Yesterday average energy consumption, indicating the general situation and house heating level. These parameters were used to heuristically form rules for the determination of the appropriate set boiler temperature. To allow for the formulation of plausibility rules (such as 'temperatures below thirty degrees Fahrenheit are rare in August') the appropriate average outdoor temperature for that season is also a system input parameter. These curves are plotted in Figure 4. Since the average temperature curves are given, no outdoor temperature needs to be measured. Hence, the outdoor temperature sensor can be eliminated. The structure of the new furnace controller is shown in Figure 5. The fuzzy controller uses a total of five inputs: four of which are derived from the energy consumption curve using conventional digital filtering techniques; the fifth is the average outdoor temperature. This input comes from a look-up table within the system clock. The output of the fuzzy system represents the estimated heat requirement of the house and corresponds to the Toutdoor value in the conventional controller (Figure 2).
Energy C o n s u m p t i o n 100% 4
75%
-
50%
-
25%
-
0%
0'h
;h
'
'
12h
24h
Daytime
Fig. 3. Actual energy consumption of the house (draft).
External Temperature
h
~ i
i
i
Jan
Fib
kt4fdl
i ~
i
I~y
i
i
i
JUFIO July ~
31
Munich i
~1
i
i
i
Od
NOv
Dec
3. Development of the system The objective of the fuzzy controller is to estimate the actual heat requirement of the
Fig. 4. Average outside temperatures in Munich.
°==
FuelValve boiler
• -2
heatcharacteristic
+2
~f(e)
Fig. 5. Schematics of the new furnace controller.
32
C. Altrock et al./Adaptive fuzzy control applied to home heating system
house. For this, if-then rules were defined to express the engineering heuristics of this parameter estimation: IF
AND AND AND AND THEN
current_energy_consumption is low medium_term_tendency is increasing short_term_tendency is decreasing yesterday_average is medium average_outside_temperature Is very_ low estimated_heat_requirement ~s medium_high.
In total, 405 rules were defined for the parameter estimation. To develop and optimize such a large system efficiently, fuzzyTECH's matrix representation was used. This technique enables rule bases to be viewed and defined graphically rather than in text form. Figure 6 shows a screen shot of such a rule matrix. In this representation, all linguistic labels of two selected linguistic variables (established heating requirement and yesterday's average energy consumption) are displayed. All other variables (medium term tendency) are kept at a selected label. The matrix may be browsed to show the entire rule base by selecting other terms for these variables. Within the matrix, a white square indicates rule plausibility whereas a black square indicates rule implausibility (not existent in the rule base). For instance, the highlighted rule in Figure 6 is valid. Its textual representation (in the lower part of the window) can be read as: IF AND THEN
medium_term_tendency IS stable yesterday_avg is medium est._heat_req. Is medium.
For the formulation of these IF-THEN rules, an initial systems prototype was built. During system optimization, however, it became apparent that some rules were more important than others and that mere rule addition/deletion was too inexact of a system-tuning method. Thus the inference strategy had to be extended to allow rules to be associated with a 'degree of support', a number between 0 and 1 which expresses the individual importance of each rule with respect to all other rules. The degree of support for each rule is indicated in the matrix by a gray-shaded square. This allows for the
est heat req.very_high
= hlah
I
,r m . a . m
Degree of Support ,SHOW... C) Ittput A(j(ire(latlon
Support
O Ca)ml)osHionwith De~ree. ol
I~gree of Support
I IF mediumlendertc
I
yesterdayav
[very-hIgh
rIHEN'
9
| I
est,heatreq.
|
~lecreasln9
low
Fig. 6. Screenshotof rule base as matrix representation.
expression of rules like: IF AND THEN
medium_term_tendency Is stable yesterday_avg is very_high est_heat_req IS between high and very_high, rather more high.
The inference method used to represent individual degrees of support is based on approximate reasoning and Fuzzy Associative Map (FAM) techniques: after fuzzification, all rule premises are calculated using the minimum operator for the representation of the linguistic AND and the maximum operator for the representation of the linguistic OR [11]. Next, the premise's degree of validity is weighted with the individual degree of support of the rule, resulting in the degree of truth for the conclusion [2, 9, 10]. In the third step, all conclusions are combined using the maximum operator. The result of this is a fuzzy set. The Center-ofMaximum defuzzification method is used to arrive at a real value from a fuzzy output [5]. The entire structure of the fuzzy controller is shown in Figure 7. In this screen shot, the large block in the middle represents the previouslydescribed rule base while the small blocks represent input and output interfaces. The icons denote the fuzzification/defuzzification methods used in the respective interfaces.
C. Altrock et al./Adaptive fuzzy control applied to home heating system
Fuzzy Logic Furnace Controller
R00
I
Fig. 7. Structureof the fuzzylogiccontroller.
33
visualization of the information flow while the system is running and all fuzzification, defuzzification and rule inference steps can be graphically cross-debugged in real-time. In addition, the fuzzy controller can be modifed and optimized 'on-the-fly' during run-time using the graphical editors. During optimization, the fuzzy logic controller was connected to a real heating system. This enabled the optimization of the system robustness against process disturbances such as: • preparation of hot water (e.g. for a bath tub), • opening of windows, • extended departure, like for vacation.
4. Implementation and optimization After completion of the design of the fuzzy controller and the definition of linguistic variables, membership functions and rules, the system was compiled to the target hardware, i.e. to 8051 assembly language. With this technology, the fuzzy controller only uses 2.1 Kbyte of the internal ROM area. Once the fuzzy controller had been linked to the entire furnace controller code, the system was optimized. To achieve the most efficient system optimization, fuzzyTECH's online module was used and the target hardware (8051-based) was connected to the developer's workstation (Windows-PC). The online technique allows for the graphical
5. Performance To evaluate system performance, both the conventional controller and the fuzzy controller were connected to a test house. One such example is shown in Figure 9. Over a period of 48 hours, three graphs were plotted: • Optimal boiler temperature (calculated from the external/internal house condition). • Set boiler temperature, as derived from the conventional controller (considering outdoor temperature). • Set boiler temperature, as derived from the fuzzy controller.
DevelopmentSystem
On-Line Link (RS 232 C)
Fig. 8. Optimizationusingthe 'Online' Technique allowsfor cross-debuggingand 'on-the-fly'modifications.
34
C. Altrock et al./Adaptive fuzzy control applied to home heating system
Boiler Temperature /
/
/Fuzzy Controller Boiler Set
Temperature
ConventionalController Boiler Set Temperature
Oh
6h
12h
18h
24h
30h
36h
42h
48h
Fig. 9. Comparative performance test (scheme).
The result of the comparative performance tests showed that the fuzzy controller was highly responsive to the actual heat requirement of the house. It was very reactive to sudden heat demand changes like the return of house inhabitants from vacation. In addition to this, the elimination of the outdoor temperature sensor saved about $30 in production costs and even more in installation costs. By setting the set boiler temperature beneath the level typically used by a conventional controller in low-load periods, the fuzzy controller actually saved energy. Long-term studies collecting statistical data for quantifying exactly how much energy per house could be saved annually are currently investigated. In addition to this, the two knobs parameterizing the heat characteristic for the individual house (cf. Figure 2) used by conventional heating systems, are not necessary with the fuzzy logic controller any more. This eases the use of the heating system, since the parameterization of the heating curves requires a expertise most home owners do not have. With these new generation of fuzzy logic heating controller, we achieved: • Improved engergy efficiency, since the fuzzy controller reduces heat production at low heat demand periods. • Improved comfort, due to the detection of sudden heat demand peaks.
• Easy setup, since the heat characteristic does not need to be parameterized manually. • Savings both in production and installation costs.
Taking into account the benefits of introducing engineering heuristics, formulated using fuzzy logic technologies, the price was rather low. In the product, the fuzzy logic controller only requires 2 KB of ROM. Using matrix rule representation and online development technology, the optimization of a complex fuzzy logic system containing 405 rules was done efficiently.
Literature [1] H.-J. Zimmermann, Fuzzy Set Theory---and its Applications, 2nd rev. Ed. (Kluwer, Boston, 1991). [2] C. v. Altrock, B. Krause and H.-J. Zimmermann, Advanced fuzzy logic control in automotive applications, IEEE Con[. on Fuzzy Systems (1992) 835-842. [3] DIN 32729, Teill, Me49-, Steuer- und Regeleinrichtungen fiir Heizungsanlagen. Witterungsgefiihrte Regelung der Kesselwasser-und Vorlauftemperatur, 1992. [4] M.M. Gupta and J. Qi, Design of fuzzy logic controllers based on generalized T-Operators, Fuzzy Sets and Systems 40 (1991) 473-489. [5] FuzzyTECH MCU-51 Edition Manual, Inform Software Corporation, Evanston, IL, 1992. [6] E.H. Mamdani and S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Man-Machine Stud. 7 (1975) 1-13.
C. Altrock et al./Adaptive fuzzy control applied to home heating system [7] Mitsumoto and H.-J. Zimmermann, Comparison of fuzzy reasoning methods, Fuzzy Sets and Systems 8 (1992) 253-285. [8] A. Nafarieh and J.M. Keller, A new approach to inference in approximate reasoning, Fuzzy Sets and Systems 41 (1991) 17-37. [9] C. v. Altrock, B. Krause and H.-J. Zimmermann, Advanced fuzzy logic control of a model car in extreme situations, Fuzzy Sets and Systems 48 (1992) 41-52. [10] C. v. Altrock and B. Krause, Online development tools
35
for fuzzy knowledge-based systems of higher order, Proc. of the 2nd Int. Conf. on Fuzzy Logic and Neural Networks, lizuka, Japan, 1992, 269-272. [11] B. Kosko, Neural Networks and Fuzzy Systems (Prentice-Hall, Englewood Cliffs, 1992, New Jersey). [12] L.A. Zadeh, Outline of a new approach to the analysis of complex systems and decision processes, IEEE Transactions on Systems, Man, and Cybernetics SMC-3 (1) (1973) 28-44.
29
Adaptive fuzzy control applied to home heating system C. v. Altrock Inform Software Corp., Evanston, IL 60201, USA
H.-O. Arend Viessmann Werke GmbH & Co., D-3559 Allendorf (Eder), Germany
B. Krause, C. Steffens and E. BehrensR6mmler INFORM GmbH, D-52076 Aachen, Germany Received May 1993 Revised August 1993
Abstract: To maximize both heating economy and comfort of a private home heating system, fuzzy-logic control has been used by a German company in a new generation of furnace controllers. The fuzzy-logic controller ensures optimal adaptation to changing customer heating demands while using one sensor less than the former generation. Both the fuzzy-logic controller and the conventional control system were implemented on a standard 8-bit microcontroller. The design, optimization and implementation of the fuzzy controller was supported by the software development system fuzzyTECH.
Keywords: Fuzzy logic control; customer habit adaptation; advanced tools; online optimization; embedded control; furnace control; climate control.
1. Introduction Most European houses have a centralized heating system which uses a furnace for diesel-type fuel to heat the water supply (boiler). From the boiler, the hot water is distributed by a pipe system to individual radiators in the rooms of the house. To meet the different needs of customer heating habits, the temperature of the furnace-heated water must constantly be adjusted in relation to the outdoor temperature (heat characteristic). To measure the outdoor Correspondence to: B. Krause, Inform GmbH, Pascalstr. 23, D-52076 Aachen, Germany.
temperature, a sensor is installed at the outside of the house. Figure 1 depicts the basic structure of such a system. The basic structure of a controller for this system is shown in Figure 2. The controller itself realizes a on-off characteristic. If the water temperature in the furnace drops to 2 Kelvin below the set temperature, the fuel valve opens and the ignition system starts the burning process. When the water temperature in the boiler itself rises to 2 Kelvin above the set temperature, the fuel valve closes. This on-off control strategy involving hysteresis minimizes the number of starts while assuring that the boiler temperature remains within the desired tolerance in contrast to a on-off control strategy without hysteresis. Although the structure of this control loop is quite simple, the task of determining the appropriate set boiler temperature is not. Since the maximum heat dissipation of the room radiators depends on the temperature of the incoming water (approximately the boiler temperature), the set point for the water temperature in the boiler must never be set so low that it cannot warm the house when necessary. On the other hand, an excessively high setting of the boiler temperature would result in energy loss in both the furnace and the piping system. Thus the set boiler temperature needs to be carefully set to ensure both user comfort and energy efficiency. In the 1950's, the German Electrical Engineering Society (VDE) defined the following procedure for this: based on the assumption that the maximum amount of heat, required by the house, depends on the outdoor temperature (Toutdoor), a parameterized function Tsetboiler = f(T,,utd,,or) was defined to adjust the set boiler temperature in relation to the outside temperature (heat characteristic) [3]. Parameters are the insulation coefficient of the house and a so-called 'comfort parameter'. The physical model of this
0165-0114/94/$07.00 ~ 1994---Elsevier Science B.V. All rights reserved SSDI: 0165-0114(93)E0181-Q
30
C. Altrock et al./Adaptive fuzzy control applied to home heating system
J
Outdoor Temperature
Fuel
=~
Circu~Pump
Furnace
Individual Radiators for Every Room
Fig. 1. Schemeof a centralized heating system. ,=
Toutdoor
:
: i i
!
:
1,
boiler
IF heat cl~arac'teHS'tic
I
J
IreCt
J T
[
i;;
Fig. 2. Blockschemeof the conventionalfurnacecontroller. is one in which the maximum amount of available heat equals the amount of heat disposed by the house plus some excess energy to compensate occasional door and window opening. Back in those days when most houses had only poor thermal insulation, the assumption, that the energy to be delivered by a heating system was largely outdoor-temperature dependent, was appropriate. Today, this is obsolete. Due to rising energy costs and environmental concerns, modern houses are built with improved insulation. Therefore, to achieve high efficiency, the outdoor temperature is not the only parameter which reflects the required energy amount. Other factors, such as ventilation, door/window openings and personal lifestyle, have to be considered as well.
2. The fuzzy controller Basically, there are two approaches for determining the appropriate set boiler temperature for a well-insulated house: • extensive use of sensors (i.e. temperature
sensors in every room) and use of a mathematical model. • definition of engineering heuristics to determine the set boiler temperature; based on a knowledge-based evaluation of existing sensor data. Since the use of extensive sensors is expensive and the construction of a comprising mathematical model is of overwhelming complexity, the second approach has been chosen for realizing the new generation of heating system controllers. The most important criteria about individual customer heat demand patterns comes from the actual energy consumption curve of the house, which is measured by the on/off-ratio of the burner. An example of such a curve is given in Figure 3. From this curve, four describing parameters are derived: (1) Current energy consumption, indicating current load. (2) Medium term tendency (I), indicating heating-up and heating-down phases. (3) Short term tendency (II), indicating disturbances like door/window openings.
C. Ahrock et al./Adaptive fuzzy control applied to home heating system
(4) Yesterday average energy consumption, indicating the general situation and house heating level. These parameters were used to heuristically form rules for the determination of the appropriate set boiler temperature. To allow for the formulation of plausibility rules (such as 'temperatures below thirty degrees Fahrenheit are rare in August') the appropriate average outdoor temperature for that season is also a system input parameter. These curves are plotted in Figure 4. Since the average temperature curves are given, no outdoor temperature needs to be measured. Hence, the outdoor temperature sensor can be eliminated. The structure of the new furnace controller is shown in Figure 5. The fuzzy controller uses a total of five inputs: four of which are derived from the energy consumption curve using conventional digital filtering techniques; the fifth is the average outdoor temperature. This input comes from a look-up table within the system clock. The output of the fuzzy system represents the estimated heat requirement of the house and corresponds to the Toutdoor value in the conventional controller (Figure 2).
Energy C o n s u m p t i o n 100% 4
75%
-
50%
-
25%
-
0%
0'h
;h
'
'
12h
24h
Daytime
Fig. 3. Actual energy consumption of the house (draft).
External Temperature
h
~ i
i
i
Jan
Fib
kt4fdl
i ~
i
I~y
i
i
i
JUFIO July ~
31
Munich i
~1
i
i
i
Od
NOv
Dec
3. Development of the system The objective of the fuzzy controller is to estimate the actual heat requirement of the
Fig. 4. Average outside temperatures in Munich.
°==
FuelValve boiler
• -2
heatcharacteristic
+2
~f(e)
Fig. 5. Schematics of the new furnace controller.
32
C. Altrock et al./Adaptive fuzzy control applied to home heating system
house. For this, if-then rules were defined to express the engineering heuristics of this parameter estimation: IF
AND AND AND AND THEN
current_energy_consumption is low medium_term_tendency is increasing short_term_tendency is decreasing yesterday_average is medium average_outside_temperature Is very_ low estimated_heat_requirement ~s medium_high.
In total, 405 rules were defined for the parameter estimation. To develop and optimize such a large system efficiently, fuzzyTECH's matrix representation was used. This technique enables rule bases to be viewed and defined graphically rather than in text form. Figure 6 shows a screen shot of such a rule matrix. In this representation, all linguistic labels of two selected linguistic variables (established heating requirement and yesterday's average energy consumption) are displayed. All other variables (medium term tendency) are kept at a selected label. The matrix may be browsed to show the entire rule base by selecting other terms for these variables. Within the matrix, a white square indicates rule plausibility whereas a black square indicates rule implausibility (not existent in the rule base). For instance, the highlighted rule in Figure 6 is valid. Its textual representation (in the lower part of the window) can be read as: IF AND THEN
medium_term_tendency IS stable yesterday_avg is medium est._heat_req. Is medium.
For the formulation of these IF-THEN rules, an initial systems prototype was built. During system optimization, however, it became apparent that some rules were more important than others and that mere rule addition/deletion was too inexact of a system-tuning method. Thus the inference strategy had to be extended to allow rules to be associated with a 'degree of support', a number between 0 and 1 which expresses the individual importance of each rule with respect to all other rules. The degree of support for each rule is indicated in the matrix by a gray-shaded square. This allows for the
est heat req.very_high
= hlah
I
,r m . a . m
Degree of Support ,SHOW... C) Ittput A(j(ire(latlon
Support
O Ca)ml)osHionwith De~ree. ol
I~gree of Support
I IF mediumlendertc
I
yesterdayav
[very-hIgh
rIHEN'
9
| I
est,heatreq.
|
~lecreasln9
low
Fig. 6. Screenshotof rule base as matrix representation.
expression of rules like: IF AND THEN
medium_term_tendency Is stable yesterday_avg is very_high est_heat_req IS between high and very_high, rather more high.
The inference method used to represent individual degrees of support is based on approximate reasoning and Fuzzy Associative Map (FAM) techniques: after fuzzification, all rule premises are calculated using the minimum operator for the representation of the linguistic AND and the maximum operator for the representation of the linguistic OR [11]. Next, the premise's degree of validity is weighted with the individual degree of support of the rule, resulting in the degree of truth for the conclusion [2, 9, 10]. In the third step, all conclusions are combined using the maximum operator. The result of this is a fuzzy set. The Center-ofMaximum defuzzification method is used to arrive at a real value from a fuzzy output [5]. The entire structure of the fuzzy controller is shown in Figure 7. In this screen shot, the large block in the middle represents the previouslydescribed rule base while the small blocks represent input and output interfaces. The icons denote the fuzzification/defuzzification methods used in the respective interfaces.
C. Altrock et al./Adaptive fuzzy control applied to home heating system
Fuzzy Logic Furnace Controller
R00
I
Fig. 7. Structureof the fuzzylogiccontroller.
33
visualization of the information flow while the system is running and all fuzzification, defuzzification and rule inference steps can be graphically cross-debugged in real-time. In addition, the fuzzy controller can be modifed and optimized 'on-the-fly' during run-time using the graphical editors. During optimization, the fuzzy logic controller was connected to a real heating system. This enabled the optimization of the system robustness against process disturbances such as: • preparation of hot water (e.g. for a bath tub), • opening of windows, • extended departure, like for vacation.
4. Implementation and optimization After completion of the design of the fuzzy controller and the definition of linguistic variables, membership functions and rules, the system was compiled to the target hardware, i.e. to 8051 assembly language. With this technology, the fuzzy controller only uses 2.1 Kbyte of the internal ROM area. Once the fuzzy controller had been linked to the entire furnace controller code, the system was optimized. To achieve the most efficient system optimization, fuzzyTECH's online module was used and the target hardware (8051-based) was connected to the developer's workstation (Windows-PC). The online technique allows for the graphical
5. Performance To evaluate system performance, both the conventional controller and the fuzzy controller were connected to a test house. One such example is shown in Figure 9. Over a period of 48 hours, three graphs were plotted: • Optimal boiler temperature (calculated from the external/internal house condition). • Set boiler temperature, as derived from the conventional controller (considering outdoor temperature). • Set boiler temperature, as derived from the fuzzy controller.
DevelopmentSystem
On-Line Link (RS 232 C)
Fig. 8. Optimizationusingthe 'Online' Technique allowsfor cross-debuggingand 'on-the-fly'modifications.
34
C. Altrock et al./Adaptive fuzzy control applied to home heating system
Boiler Temperature /
/
/Fuzzy Controller Boiler Set
Temperature
ConventionalController Boiler Set Temperature
Oh
6h
12h
18h
24h
30h
36h
42h
48h
Fig. 9. Comparative performance test (scheme).
The result of the comparative performance tests showed that the fuzzy controller was highly responsive to the actual heat requirement of the house. It was very reactive to sudden heat demand changes like the return of house inhabitants from vacation. In addition to this, the elimination of the outdoor temperature sensor saved about $30 in production costs and even more in installation costs. By setting the set boiler temperature beneath the level typically used by a conventional controller in low-load periods, the fuzzy controller actually saved energy. Long-term studies collecting statistical data for quantifying exactly how much energy per house could be saved annually are currently investigated. In addition to this, the two knobs parameterizing the heat characteristic for the individual house (cf. Figure 2) used by conventional heating systems, are not necessary with the fuzzy logic controller any more. This eases the use of the heating system, since the parameterization of the heating curves requires a expertise most home owners do not have. With these new generation of fuzzy logic heating controller, we achieved: • Improved engergy efficiency, since the fuzzy controller reduces heat production at low heat demand periods. • Improved comfort, due to the detection of sudden heat demand peaks.
• Easy setup, since the heat characteristic does not need to be parameterized manually. • Savings both in production and installation costs.
Taking into account the benefits of introducing engineering heuristics, formulated using fuzzy logic technologies, the price was rather low. In the product, the fuzzy logic controller only requires 2 KB of ROM. Using matrix rule representation and online development technology, the optimization of a complex fuzzy logic system containing 405 rules was done efficiently.
Literature [1] H.-J. Zimmermann, Fuzzy Set Theory---and its Applications, 2nd rev. Ed. (Kluwer, Boston, 1991). [2] C. v. Altrock, B. Krause and H.-J. Zimmermann, Advanced fuzzy logic control in automotive applications, IEEE Con[. on Fuzzy Systems (1992) 835-842. [3] DIN 32729, Teill, Me49-, Steuer- und Regeleinrichtungen fiir Heizungsanlagen. Witterungsgefiihrte Regelung der Kesselwasser-und Vorlauftemperatur, 1992. [4] M.M. Gupta and J. Qi, Design of fuzzy logic controllers based on generalized T-Operators, Fuzzy Sets and Systems 40 (1991) 473-489. [5] FuzzyTECH MCU-51 Edition Manual, Inform Software Corporation, Evanston, IL, 1992. [6] E.H. Mamdani and S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Man-Machine Stud. 7 (1975) 1-13.
C. Altrock et al./Adaptive fuzzy control applied to home heating system [7] Mitsumoto and H.-J. Zimmermann, Comparison of fuzzy reasoning methods, Fuzzy Sets and Systems 8 (1992) 253-285. [8] A. Nafarieh and J.M. Keller, A new approach to inference in approximate reasoning, Fuzzy Sets and Systems 41 (1991) 17-37. [9] C. v. Altrock, B. Krause and H.-J. Zimmermann, Advanced fuzzy logic control of a model car in extreme situations, Fuzzy Sets and Systems 48 (1992) 41-52. [10] C. v. Altrock and B. Krause, Online development tools
35
for fuzzy knowledge-based systems of higher order, Proc. of the 2nd Int. Conf. on Fuzzy Logic and Neural Networks, lizuka, Japan, 1992, 269-272. [11] B. Kosko, Neural Networks and Fuzzy Systems (Prentice-Hall, Englewood Cliffs, 1992, New Jersey). [12] L.A. Zadeh, Outline of a new approach to the analysis of complex systems and decision processes, IEEE Transactions on Systems, Man, and Cybernetics SMC-3 (1) (1973) 28-44.