- Email: [email protected]
Use of expert systems for the selection and the design of solar domestic hot water systems
Pergamon
PII: SU038-092X(96)00035-7
Solar Energy Vol. 57, No. 1, pp. 1-8, 1996 Copyright 0 1996 Elsevier Science Ltd
Printed in Great Britain. All rights reserved 0038-092X/96 %15.00+0.00
USE OF EXPERT SYSTEMS FOR THE SELECTION AND THE DESIGN OF SOLAR DOMESTIC HOT WATER SYSTEMS S. PANTELIOU> Machine
Design Laboratory,
A. DENTSORAS and E. DASKALOPOULOS
Mechanical
Engineering Department, Greece
(Communicated
University
of Patras,
Patras
26110,
by J. Luther)
Abstract-The aim of this article is the study of the application of expert systems to a mechanical engineering research domain with practical and commercial interest, such as design and manufacturing of Solar Domestic Hot Water (SDHW) Systems. The issues studied were the selection and the design of SDHW systems. The application of an expert system was explored. Frame and class formalism was used for knowledge representation together with forward and backward chaining techniques for drawing conclusions and utilizing the accumulated information present. The appropriate computer program was developed to yield the selection of SDHW systems using the software tool LEONARDO 3.0 (1989), an integrated environment for the development of expert systems. The developed program was tested with data according to the Greek standard ELOT corresponding to the ISO/DIS 9459-2 and it performed successfully for 21 SDHW systems available on the Greek market. Apart from the possibility of selection of a SDHW system, the program also supports the facility for updating its knowledge base with new data so that it can be adapted to changes appearing on the market. The program proved to be functional and user friendly to a high degree. Copyright 0 1996 Elsevier Science Ltd.
1. INTRODUCTION
1.1. SDH Wsystems
The domain of SDHW systems is considered as defined by Gillett and Moon (1985) and Aranovitch et al. (1989). 1.2. Expert systems An expert system is a computer program incorporating the knowledge of an expert in a specific subject with a view to solve problems or give advice as proposed by Jackson (1990). Thus an expert system is a program capable of emulating human cognitive skills such as problem solving, visual perception and language understanding. In any case, one must distinguish it from conventional application programs, as it exhibits certain characteristics that the latter do not. Specifically an expert system, according to Pham (1988), is as follows: Designed for solving complex problems ordinarily requiring human intelligence. Embodying both expert knowledge and logically inferring means; the former should be stored explicitly in a symbolic declarative language; the latter would consist of heuristic search and reasoning procedures for utilizing the stored information. Capable of achieving high performance in narrowly specified domains of incremental ‘Author
to whom correspondence
should
be addressed.
development, dealing with incomplete or uncertain data, handling unforeseen situations and explaining or justifying its results. Limited to a specific area of human expertise. Designed to grow on an evolutionary basis, improving its “expertise” as it grows. Using facts and rules to represent the expertise. Able to use other knowledge representation methods to handle knowledge which is not well expressed as rules. What is expected from an expert system is to deal with problems of scientific or commercial nature, to provide solutions in a reasonable time and to be right about them, as a human expert does. Moreover, it should be able to provide explanations about the conclusions reached, by displaying in some way all the steps of its reasoning process, and it should be able to provide explanations about the course of action it will follow according to the user’s answers to the questions that the system is programmed to ask, as proposed by Jackson (1990) and Pham (1988). An expert system usually consists of a knowledge base, an inference mechanism and a user interface according to Pham (1988). The knowledge base usually contains two different databases, a static and a dynamic one. The static database contains the knowledge about the domain, represented in a certain formalism. It is created once, when the system is being devel-
2
S. Panteliou
oped by the user, but it can be modified at runtime (with addition of new facts, deletion of some part of the existing knowledge or alteration of some part of it). The dynamic database may be enriched during each execution of the program but the information is lost when the execution is terminated. It is used to store all information obtained from the user, as well as intermediate conclusions (facts) that are inferred during the reasoning process. The inference mechanism contains the control methods that indicate how the present knowledge is to be processed, in order to obtain solutions and conclusions to the problem. It reflects the way in which the system reasons on the acquired knowledge, and it is interrelated with the human way of reasoning. The user interface provides the user with the explanations on the system’s performance, obtains from the user the information that the system needs in order to perform and presents the results obtained during the reasoning process.
2. EXPDHWS: EXPERT SYSTEM FOR THE SELECTION AND DESIGN OF SOLAR DOMESTIC HOT WATER SYSTEMS A prototype computer program, namely the expert system, EXPDHWS, was developed, to support the selection of SDHW systems, using the software tool LEONARDO (1989). The expert system was tested with data according to the Greek standard ELOT 879 and it performed successfully for 21 SDHW systems available to the Greek market. The system was built up in a modular way by fully exploiting the capabilities offered by the aforementioned software tool. In order to represent the knowledge about the problem, a set of rules has been used together with a “frame” representation formalism. It is a common practice in expert systems to represent the knowledge contained through rules that have the form “IF hypothesis THEN conclusion” where either hypothesis and/or conclusion can consist of one or more sentences. Moreover, the conclusion of a rule can be the hypothesis of another, so that multiple-level rule dependencies can be created. A typical example of a rule is given in Fig. 1. The rule has two hypotheses and one conclusion and refers to the assignment of a value to object obj3. Here, objl, obj2, obj3 are objects while “high”, 2, “medium” are corresponding values. The obj3, in turn, together with the value assigned, could be
et al.
if obj 1 is “high” and obj2 = 2 then obj3 is “medium” Fig. 1. A typical
example
of a rule.
involved in another rule’s hypothesis or redefined in the conclusion’s part of another rule. In most of the cases, the reasoning process of an expert system is based on a forward and/or backward chaining of the available rules. This technique is a common search technique used in artificial intelligence when one or more solutions for a posed problem are required. Since rules refer mainly to the attributes of physical or logical objects, there must be a formal way of representing these objects and their attributes. Through the “frame” formalism an object is represented as a collection of its general and domain-related attributes. Additionally, rules, connections with other objects and procedures can be contained and declared within the context of the object’s frame. Usually, attribute names are given as “slots” which then get their values either by the user or by rules and procedures activated during the system’s execution. References to other objects, rules or procedures are also given through slots. The frame knowledge representation formalism has been established as a classical artificial intelligence technique and is encountered in many similar situations as proposed by Rich et al. (1991) and Winstanley (1991). The typical form of a frame referring to an object is shown in Fig. 2. Here only some of the slots are already filled with values, while others will be filled during the system’s execution. The available data about SDHW systems are classified in two main groups. The first group, named system, contains all specific manufacturing attributes related to the collector and to the storage tank. The second group, named heatName: LongName: Type: Value: Certainty: Derived From: Default Value: Fixed Value: Allowed Value: Compute Value: Rule Set: Fig. 2. The typical
objl Text medium 1.0 low
low,medium,high
form of a frame.
Use of expert systems for the selection and the design of solar domestic hot water systems
lossl, contains measured data related to the parameters which are needed for the estimation of the heat transfer coefficient of the system. This coefficient is not, at present, calculated by the system, although the appropriate extra subroutine could be included for this purpose in a future version. The specific parameters which were used for the selection of a SDHW system are the following: (i) Manufacturing characteristics (system): -collector type -number of collectors -aperture area -number of collector pipes -specific dimensions of pipes in collector -storage tank type -storage tank volumetric capacity -storage tank insulation material -storage tank’s thermal insulation thickness -electric heater power -heat exchanger type. (ii) Energy characteristics (heatloss 1); this group of characteristics is used twice, covering the cases of collectors connected or not connected to the system: -inlet water temperature of storage (T) -outlet water temperature of storage (Tr) -average ambient temperature (T,,,,) -wind speed over the storage tank (V,) -test period during which the experimentation lasted -heat loss coefficient of the SDHW system. It is a common practice, when developing expert systems, to organize the available objects and, as a consequence, the knowledge they represent in an hierarchical way. This means that some objects can be defined as “children” (or members) of some more general objects which are then called their “parents”. This relation forms a kind of inheritance which can be extended to several levels, depending on the case under consideration. While preserving the term “object” for a simple object, the term “class object” (or simply “class”) denotes a parent object, whose attributes are inherited to the simple objects belonging to that class. An inheritance relation example is given in Fig. 3. Here, the slot “Members:” contains all objects that are “children” (or members) of the class object named “General Object”, while the list of attribute slots and their values (here “good” for attribute 2) are inherited by the object “objl”. The description of the objects with the method described above facilitates the reasoning process, minimizes the number of rules needed and,
3
what is more important, fully exploits the physical attribute connection that exists between physical objects in many engineering problems. Three class objects have been structured for the present application. Class object questions include as members all the important questions that must be posed to the user during the reasoning process. In Fig. 4, a question prompt about the aperture area is shown as it appears during program execution. The other two class objects, namely system and heatlossl include as members all manufacturing and energy characteristics of the available systems stored in the data files. The database of the expert system consists of several files containing data about SDHW systems covering a wide range of existing systems. These files have been constructed for each attribute respectively. An example of such a data file is given below. any,24,16,29,any,any,16,24,29,16,11,8,any,any, any,13,10,any,16,16,any 3. QUESTION-ANSWER
PROCESS
While querying about input data, the program has the ability of asking both first-level and second-level questions in order to adapt the required amount of input data from the user to the demands of the current case. First, a question must be answered by the user so that the system can decide whether the reasoning process will be guided to a more detailed secondlevel set of questions or not. For the first case, subsequent second-level questions will be posed in order to gain more knowledge about the system attributes. The prompt of a first-level question has already been shown in Fig. 4, while a second-level question prompt concerning heat losses is given in Fig. 5. The question prompt is incorporated into the object information 3 as the content of the slot QueryPreface:. The separation of the questions posed to the user in two distinct levels facilitates significantly the reasoning process of the expert system and, what is more important, minimizes the execution time. Additionally, depending on the case under consideration, the user has only to provide the minimum required information for the expert system to come up with a conclusion about a solar system that satisfies the posed requirements. The program is structured in two different parts. The first refers to the SDHW system selection and the second to the enrichment of
S. Panteliou
et al.
Name : General_Object value: _\ Members: :dsjl~~ckzi&~i@~~. Paemberslots
:
Name: --------
-\
attiM:
I
attriM&?:
+
at&&_&&:
Fig. 3. The typical
value:
“good”
inheritance
relation
rSlots
,
at&b&&
1
attrihlte2:
I
attrib-lte3:
between
:
a class object and its members.
Please define the aperture area of the collector you consider best for your system. Unfortunately there is no information about the specific dimensions. You are asked to choose the appropriate value from the given list below. If you warn to choose one of the options, simply type the option at the queryprompt. or if you don’t care just press ENTER. Also you can choose multiple options by typing them with commas. CAPITALS are not allowed. Fig.
4. A
question
prompt
about
the aperture
area.
Name: information3 LongName: Type: Value: Certainty: DerivedProm: Allowed Value: yes,no ForbidUnk:
QueryPrompt: Can you define any informations about the heat losses ? QueryPreface: Answering yes to this question means that you have details about the inlet and outlet water temperature of storage, the average ambient temperature, the wind speed over the storage tank, the heat loss coefficient of the system while the collector is connected to the system or it is not. If this answer is “no” then these questions will be skipped and all the systems will be selected for your application. Please make your choice. Fig. 5. A first-level question concerning
the database files. The operation of these two distinct parts is described in the next section. 4. SELECTION OF A SDHW SYSTEM
For the selection of a certain SDHW system, the user is asked to input values of certain characteristics which will subsequently determine the appropriate system for a specific application. First, an initialization process takes place during which the members of the class object system are dynamically created according to the information contained in the corresponding database file. By the completion of this process,
information
about
heat
losses.
all stored systems are available to the user and the main reasoning process starts. The main SDHW system selection process is repetitive. A brief help text (see Fig. 6) together with the corresponding options list are always available to the user during the system execution. The developed expert system uses a simple matching technique during comparison of user inputs toward the attributes of the available systems. For every SDHW system and if the matching is successful, it is considered as accepted until the next matching trial etc. Any time during the system’s execution, the systems that have satisfied the posed requirements are
Use of expert systems for the selection
and the design of solar domestic
hot water systems
5
Please define the number of pipes that will be assembled in the collector. The fluid in a collector usually flows from the bottom to the top, either through an array of parallel tubes (risers), or through a single tube passing over the absorber along a serpentine path. These tubes should be arranged so that air will rise freely up and out of the absorber. CHOOSE the appropriate value from the given list below. If you want to choose one of the options, simply type the option at the query prompt, or if you don’t care, just press ENTER. You can also choose multiple options by typing them with commas. CAPITALS are not allowed. Fig. 6. Screen help text concerning
kept in a solution list, which after the completion of the selection process, contains one or more SDHW systems, which in turn are then considered to be the solution(s) for the current system running. All systems that fail to match the user requirements in any step of this repetitive process, are completely rejected. It must be noticed however that the expert system never fails, since the user is always guided to exclusively acceptable input values according to the attributes of the already-up to this step-acceptable SDHP’ systems. The selection process is shown in Fig. 7. 5. ENRICHMENT
OF
DATABASE
FILES
The user is queried to add new systems in the database by completing relevant forms, through a very simple procedure. He only has to know all the attribute values which are necessary to define completely a system according to the program’s logic (Fig. 8). When the user finishes the completion of data forms, the program will open the database files and append the attribute values of the new system to each attribute file respectively. These new values can then be used in any subsequent program execution. Theoretically the database can sustain any number of systems, which the user considers to be satisfactory for a complete selection of SDHW system. In this paper the characteristics of 21 SDHW systems have been incorporated into the database files. All the necessary data included have been collected from Pafelias (1991). 6. APPLICATION
EXAMPLE
At this point an example is given, demonstrating the application of the existing expert system “EXPSDHW” to the selection of a SDHW system. The first level question referring to the manufacturing characteristics is given below: Can you define any information about manufacturing characteristics? YES/NO
the
the choice of the number
of pipes.
Answering YES to this question, means that you have details about all the manufacturing characteristics specified in the previous analysis and the system will proceed with second-level questions, while NO means that all these questions will be skipped and all the SDHW systems, which have been introduced in the database, will be selected for the application. Below, an example is given of a second-level question that the user must answer. At this point, you have to define the required number of collectors. In most cases one or two collectors are used. It is obvious that the choice about the number of collectors, must be made according to the “sdhw-system’s” efficiency and the economic benefit of the installation. You are asked to choose the appropriate value from the given list below. If you want to choose one of the options, simply type the option at the query prompt, or if you don’t care just press ENTER. Also you can choose multiple options by typing them with commas. CAPITALS are not allowed. Please choose number
of collectors?
ONE/TWO/MORE
For the present application it is assumed that the user’s requirements for the manufacturing characteristics of the SDHW system are described as follows: collector type: flat plate; number of collectors: two; aperture area: 2.68 m2; number of pipes: 8; pipe diameter: 15 mm; tank type: horizontal; volumetric capacity: 160 1; insulation material: polyurethane; insulation thickness: 55 mm; exchanger type: jacket; heater power: 4 kW. Only the systems that are consistent with the above list will be selected. The above process will be repeated for the energy characteristics group and finally the output screen of Fig. 9 will appear indicating the optimal proposed system among those incorporated up to now to the expert system.
S. Panteliou et al.
FIRST LEVEL QUESTION=YES
OPENDATABASE FILE FOR SECOND LEVEL ATTRIBUTES
??
cr 3UTE
I
USER’S INPUT
hb
LOAD MEMBER ATTRIBUTE
NEXT MEMBER
No
Fig. 7. Flow chart of the repetitive process for SDHW selection.
7. CONCLUSIONS
According to the previous analysis and the corresponding application, the following major remarks can be stated. The adoption of expert system techniques to help the design of SDHW systems seems to be promising enough since it enables the combination of conventional design methods and procedures with design rules that incorporate the human expertise on the field. This combination configures a unique design tool that can accelerate, improve and optimize the quality of the final design. The choice of class objects and frame formalism as a basis for the development of the current expert system has been proven to be the optimal
path for representing both qualitative and quantitative characteristics of the SDHW systems. Moreover, the use of the conventional “IFTHEN” form for the knowledge base was found to be the simplest way to capture the expert design knowledge. In order to minimize the time required for the initial data input and to make the process more user friendly, the query process includes two levels of questions. Also the program guides the user continuously by giving them reports of the available choices, avoiding the input of non-acceptable values. In this way the user can configure a desirable design scheme, the characteristics of which, in any case, will be extracted from the already available SDHW systems.
Use of expert systems
for the selection
and the design of solar domestic
SET 1: COLLECTOR Define Define Define Define Define
the the the the the
hot water systems
7
INFORMATION
collector type: number of collectors: aperture area: number of pipes: pipe diameter:
Fig. 8. A typical input form concerning
information about a new collector enrichment process.
Fig. 9. Example
of an optimal
The program also contains a dynamic database. The user can enrich it by inserting any new SDHW system he desires, under the only restriction that he uses the specific enrichment procedure of the program. According to this procedure all the information obtained from the user, as well as intermediate conclusions inferred during the reasoning process, will be used during each execution of the program, but will be lost when the execution is terminated. It is very simple to add new attributes. The user can add new constraints for the selection part, by adding new members to class object questions, and the appropriate data files to the database. The program code is relatively small, since the member creation process takes place during runtime and the data for the available SDHW systems are kept in a separate database. Finally the aforementioned application of expert systems to the design of SDHW systems is only a preliminary proposal which is indicative of the possible extension of the case. In a
proposed
as it appears
during the database
system
future extension, the system can comprise some additional features, i.e. a learning-by-examples facility, as well as reasoning under uncertainty which will bring it closer to real-world situations and increase its reliability. Acknowledgements-The financial contribution of ITA (Institute for Technological Applications), branch of GPC (Greek Productivity Center), is greatly acknowledged.
REFERENCES Aranovitch E., Gilhaert G., Gillett W. B. and Bates J. E. (1989) Recommendations for performance and durability tests of solar collectors and water heating systems. ELOT (Greek Standard Organization) 879, Solar energy, domestic hot water systems, procedure for the estimation of the system’s efficiency and prediction of the annual energy consumption. Gillett W. B. and Moon J. E. (1985) Solar Collectors. Test Methods und Design Guidelines. D. Reidel Publishing Company, for the Commission of the European Communities. Jackson P. (1990) Introduction to Expert Systems, pp. 3-5. Addison Wesley Publishing Company.
8
S. Panteliou ef al.
LEONARDO. Creative Logic Ltd. Pafelias T. ( 1991) Domestic hot water systems, measurements according to ELOT 879, summary of results and conclusions, Vol. A, CRES (Center of Renewable Energy Sources) Athens. Pham D. T. (1988) Expert Systems in Engineering. IFS Publications, Springer, Berlin.
Rich E. and Knight K. (1991) Artificial Intelligence. McGraw-Hill, New York. Winstanley G. (Ed.) (1991) Artificial Intelligence in Engineering. John Wiley & Sons, Chichester.
PII: SU038-092X(96)00035-7
Solar Energy Vol. 57, No. 1, pp. 1-8, 1996 Copyright 0 1996 Elsevier Science Ltd
Printed in Great Britain. All rights reserved 0038-092X/96 %15.00+0.00
USE OF EXPERT SYSTEMS FOR THE SELECTION AND THE DESIGN OF SOLAR DOMESTIC HOT WATER SYSTEMS S. PANTELIOU> Machine
Design Laboratory,
A. DENTSORAS and E. DASKALOPOULOS
Mechanical
Engineering Department, Greece
(Communicated
University
of Patras,
Patras
26110,
by J. Luther)
Abstract-The aim of this article is the study of the application of expert systems to a mechanical engineering research domain with practical and commercial interest, such as design and manufacturing of Solar Domestic Hot Water (SDHW) Systems. The issues studied were the selection and the design of SDHW systems. The application of an expert system was explored. Frame and class formalism was used for knowledge representation together with forward and backward chaining techniques for drawing conclusions and utilizing the accumulated information present. The appropriate computer program was developed to yield the selection of SDHW systems using the software tool LEONARDO 3.0 (1989), an integrated environment for the development of expert systems. The developed program was tested with data according to the Greek standard ELOT corresponding to the ISO/DIS 9459-2 and it performed successfully for 21 SDHW systems available on the Greek market. Apart from the possibility of selection of a SDHW system, the program also supports the facility for updating its knowledge base with new data so that it can be adapted to changes appearing on the market. The program proved to be functional and user friendly to a high degree. Copyright 0 1996 Elsevier Science Ltd.
1. INTRODUCTION
1.1. SDH Wsystems
The domain of SDHW systems is considered as defined by Gillett and Moon (1985) and Aranovitch et al. (1989). 1.2. Expert systems An expert system is a computer program incorporating the knowledge of an expert in a specific subject with a view to solve problems or give advice as proposed by Jackson (1990). Thus an expert system is a program capable of emulating human cognitive skills such as problem solving, visual perception and language understanding. In any case, one must distinguish it from conventional application programs, as it exhibits certain characteristics that the latter do not. Specifically an expert system, according to Pham (1988), is as follows: Designed for solving complex problems ordinarily requiring human intelligence. Embodying both expert knowledge and logically inferring means; the former should be stored explicitly in a symbolic declarative language; the latter would consist of heuristic search and reasoning procedures for utilizing the stored information. Capable of achieving high performance in narrowly specified domains of incremental ‘Author
to whom correspondence
should
be addressed.
development, dealing with incomplete or uncertain data, handling unforeseen situations and explaining or justifying its results. Limited to a specific area of human expertise. Designed to grow on an evolutionary basis, improving its “expertise” as it grows. Using facts and rules to represent the expertise. Able to use other knowledge representation methods to handle knowledge which is not well expressed as rules. What is expected from an expert system is to deal with problems of scientific or commercial nature, to provide solutions in a reasonable time and to be right about them, as a human expert does. Moreover, it should be able to provide explanations about the conclusions reached, by displaying in some way all the steps of its reasoning process, and it should be able to provide explanations about the course of action it will follow according to the user’s answers to the questions that the system is programmed to ask, as proposed by Jackson (1990) and Pham (1988). An expert system usually consists of a knowledge base, an inference mechanism and a user interface according to Pham (1988). The knowledge base usually contains two different databases, a static and a dynamic one. The static database contains the knowledge about the domain, represented in a certain formalism. It is created once, when the system is being devel-
2
S. Panteliou
oped by the user, but it can be modified at runtime (with addition of new facts, deletion of some part of the existing knowledge or alteration of some part of it). The dynamic database may be enriched during each execution of the program but the information is lost when the execution is terminated. It is used to store all information obtained from the user, as well as intermediate conclusions (facts) that are inferred during the reasoning process. The inference mechanism contains the control methods that indicate how the present knowledge is to be processed, in order to obtain solutions and conclusions to the problem. It reflects the way in which the system reasons on the acquired knowledge, and it is interrelated with the human way of reasoning. The user interface provides the user with the explanations on the system’s performance, obtains from the user the information that the system needs in order to perform and presents the results obtained during the reasoning process.
2. EXPDHWS: EXPERT SYSTEM FOR THE SELECTION AND DESIGN OF SOLAR DOMESTIC HOT WATER SYSTEMS A prototype computer program, namely the expert system, EXPDHWS, was developed, to support the selection of SDHW systems, using the software tool LEONARDO (1989). The expert system was tested with data according to the Greek standard ELOT 879 and it performed successfully for 21 SDHW systems available to the Greek market. The system was built up in a modular way by fully exploiting the capabilities offered by the aforementioned software tool. In order to represent the knowledge about the problem, a set of rules has been used together with a “frame” representation formalism. It is a common practice in expert systems to represent the knowledge contained through rules that have the form “IF hypothesis THEN conclusion” where either hypothesis and/or conclusion can consist of one or more sentences. Moreover, the conclusion of a rule can be the hypothesis of another, so that multiple-level rule dependencies can be created. A typical example of a rule is given in Fig. 1. The rule has two hypotheses and one conclusion and refers to the assignment of a value to object obj3. Here, objl, obj2, obj3 are objects while “high”, 2, “medium” are corresponding values. The obj3, in turn, together with the value assigned, could be
et al.
if obj 1 is “high” and obj2 = 2 then obj3 is “medium” Fig. 1. A typical
example
of a rule.
involved in another rule’s hypothesis or redefined in the conclusion’s part of another rule. In most of the cases, the reasoning process of an expert system is based on a forward and/or backward chaining of the available rules. This technique is a common search technique used in artificial intelligence when one or more solutions for a posed problem are required. Since rules refer mainly to the attributes of physical or logical objects, there must be a formal way of representing these objects and their attributes. Through the “frame” formalism an object is represented as a collection of its general and domain-related attributes. Additionally, rules, connections with other objects and procedures can be contained and declared within the context of the object’s frame. Usually, attribute names are given as “slots” which then get their values either by the user or by rules and procedures activated during the system’s execution. References to other objects, rules or procedures are also given through slots. The frame knowledge representation formalism has been established as a classical artificial intelligence technique and is encountered in many similar situations as proposed by Rich et al. (1991) and Winstanley (1991). The typical form of a frame referring to an object is shown in Fig. 2. Here only some of the slots are already filled with values, while others will be filled during the system’s execution. The available data about SDHW systems are classified in two main groups. The first group, named system, contains all specific manufacturing attributes related to the collector and to the storage tank. The second group, named heatName: LongName: Type: Value: Certainty: Derived From: Default Value: Fixed Value: Allowed Value: Compute Value: Rule Set: Fig. 2. The typical
objl Text medium 1.0 low
low,medium,high
form of a frame.
Use of expert systems for the selection and the design of solar domestic hot water systems
lossl, contains measured data related to the parameters which are needed for the estimation of the heat transfer coefficient of the system. This coefficient is not, at present, calculated by the system, although the appropriate extra subroutine could be included for this purpose in a future version. The specific parameters which were used for the selection of a SDHW system are the following: (i) Manufacturing characteristics (system): -collector type -number of collectors -aperture area -number of collector pipes -specific dimensions of pipes in collector -storage tank type -storage tank volumetric capacity -storage tank insulation material -storage tank’s thermal insulation thickness -electric heater power -heat exchanger type. (ii) Energy characteristics (heatloss 1); this group of characteristics is used twice, covering the cases of collectors connected or not connected to the system: -inlet water temperature of storage (T) -outlet water temperature of storage (Tr) -average ambient temperature (T,,,,) -wind speed over the storage tank (V,) -test period during which the experimentation lasted -heat loss coefficient of the SDHW system. It is a common practice, when developing expert systems, to organize the available objects and, as a consequence, the knowledge they represent in an hierarchical way. This means that some objects can be defined as “children” (or members) of some more general objects which are then called their “parents”. This relation forms a kind of inheritance which can be extended to several levels, depending on the case under consideration. While preserving the term “object” for a simple object, the term “class object” (or simply “class”) denotes a parent object, whose attributes are inherited to the simple objects belonging to that class. An inheritance relation example is given in Fig. 3. Here, the slot “Members:” contains all objects that are “children” (or members) of the class object named “General Object”, while the list of attribute slots and their values (here “good” for attribute 2) are inherited by the object “objl”. The description of the objects with the method described above facilitates the reasoning process, minimizes the number of rules needed and,
3
what is more important, fully exploits the physical attribute connection that exists between physical objects in many engineering problems. Three class objects have been structured for the present application. Class object questions include as members all the important questions that must be posed to the user during the reasoning process. In Fig. 4, a question prompt about the aperture area is shown as it appears during program execution. The other two class objects, namely system and heatlossl include as members all manufacturing and energy characteristics of the available systems stored in the data files. The database of the expert system consists of several files containing data about SDHW systems covering a wide range of existing systems. These files have been constructed for each attribute respectively. An example of such a data file is given below. any,24,16,29,any,any,16,24,29,16,11,8,any,any, any,13,10,any,16,16,any 3. QUESTION-ANSWER
PROCESS
While querying about input data, the program has the ability of asking both first-level and second-level questions in order to adapt the required amount of input data from the user to the demands of the current case. First, a question must be answered by the user so that the system can decide whether the reasoning process will be guided to a more detailed secondlevel set of questions or not. For the first case, subsequent second-level questions will be posed in order to gain more knowledge about the system attributes. The prompt of a first-level question has already been shown in Fig. 4, while a second-level question prompt concerning heat losses is given in Fig. 5. The question prompt is incorporated into the object information 3 as the content of the slot QueryPreface:. The separation of the questions posed to the user in two distinct levels facilitates significantly the reasoning process of the expert system and, what is more important, minimizes the execution time. Additionally, depending on the case under consideration, the user has only to provide the minimum required information for the expert system to come up with a conclusion about a solar system that satisfies the posed requirements. The program is structured in two different parts. The first refers to the SDHW system selection and the second to the enrichment of
S. Panteliou
et al.
Name : General_Object value: _\ Members: :dsjl~~ckzi&~i@~~. Paemberslots
:
Name: --------
-\
attiM:
I
attriM&?:
+
at&&_&&:
Fig. 3. The typical
value:
“good”
inheritance
relation
rSlots
,
at&b&&
1
attrihlte2:
I
attrib-lte3:
between
:
a class object and its members.
Please define the aperture area of the collector you consider best for your system. Unfortunately there is no information about the specific dimensions. You are asked to choose the appropriate value from the given list below. If you warn to choose one of the options, simply type the option at the queryprompt. or if you don’t care just press ENTER. Also you can choose multiple options by typing them with commas. CAPITALS are not allowed. Fig.
4. A
question
prompt
about
the aperture
area.
Name: information3 LongName: Type: Value: Certainty: DerivedProm: Allowed Value: yes,no ForbidUnk:
QueryPrompt: Can you define any informations about the heat losses ? QueryPreface: Answering yes to this question means that you have details about the inlet and outlet water temperature of storage, the average ambient temperature, the wind speed over the storage tank, the heat loss coefficient of the system while the collector is connected to the system or it is not. If this answer is “no” then these questions will be skipped and all the systems will be selected for your application. Please make your choice. Fig. 5. A first-level question concerning
the database files. The operation of these two distinct parts is described in the next section. 4. SELECTION OF A SDHW SYSTEM
For the selection of a certain SDHW system, the user is asked to input values of certain characteristics which will subsequently determine the appropriate system for a specific application. First, an initialization process takes place during which the members of the class object system are dynamically created according to the information contained in the corresponding database file. By the completion of this process,
information
about
heat
losses.
all stored systems are available to the user and the main reasoning process starts. The main SDHW system selection process is repetitive. A brief help text (see Fig. 6) together with the corresponding options list are always available to the user during the system execution. The developed expert system uses a simple matching technique during comparison of user inputs toward the attributes of the available systems. For every SDHW system and if the matching is successful, it is considered as accepted until the next matching trial etc. Any time during the system’s execution, the systems that have satisfied the posed requirements are
Use of expert systems for the selection
and the design of solar domestic
hot water systems
5
Please define the number of pipes that will be assembled in the collector. The fluid in a collector usually flows from the bottom to the top, either through an array of parallel tubes (risers), or through a single tube passing over the absorber along a serpentine path. These tubes should be arranged so that air will rise freely up and out of the absorber. CHOOSE the appropriate value from the given list below. If you want to choose one of the options, simply type the option at the query prompt, or if you don’t care, just press ENTER. You can also choose multiple options by typing them with commas. CAPITALS are not allowed. Fig. 6. Screen help text concerning
kept in a solution list, which after the completion of the selection process, contains one or more SDHW systems, which in turn are then considered to be the solution(s) for the current system running. All systems that fail to match the user requirements in any step of this repetitive process, are completely rejected. It must be noticed however that the expert system never fails, since the user is always guided to exclusively acceptable input values according to the attributes of the already-up to this step-acceptable SDHP’ systems. The selection process is shown in Fig. 7. 5. ENRICHMENT
OF
DATABASE
FILES
The user is queried to add new systems in the database by completing relevant forms, through a very simple procedure. He only has to know all the attribute values which are necessary to define completely a system according to the program’s logic (Fig. 8). When the user finishes the completion of data forms, the program will open the database files and append the attribute values of the new system to each attribute file respectively. These new values can then be used in any subsequent program execution. Theoretically the database can sustain any number of systems, which the user considers to be satisfactory for a complete selection of SDHW system. In this paper the characteristics of 21 SDHW systems have been incorporated into the database files. All the necessary data included have been collected from Pafelias (1991). 6. APPLICATION
EXAMPLE
At this point an example is given, demonstrating the application of the existing expert system “EXPSDHW” to the selection of a SDHW system. The first level question referring to the manufacturing characteristics is given below: Can you define any information about manufacturing characteristics? YES/NO
the
the choice of the number
of pipes.
Answering YES to this question, means that you have details about all the manufacturing characteristics specified in the previous analysis and the system will proceed with second-level questions, while NO means that all these questions will be skipped and all the SDHW systems, which have been introduced in the database, will be selected for the application. Below, an example is given of a second-level question that the user must answer. At this point, you have to define the required number of collectors. In most cases one or two collectors are used. It is obvious that the choice about the number of collectors, must be made according to the “sdhw-system’s” efficiency and the economic benefit of the installation. You are asked to choose the appropriate value from the given list below. If you want to choose one of the options, simply type the option at the query prompt, or if you don’t care just press ENTER. Also you can choose multiple options by typing them with commas. CAPITALS are not allowed. Please choose number
of collectors?
ONE/TWO/MORE
For the present application it is assumed that the user’s requirements for the manufacturing characteristics of the SDHW system are described as follows: collector type: flat plate; number of collectors: two; aperture area: 2.68 m2; number of pipes: 8; pipe diameter: 15 mm; tank type: horizontal; volumetric capacity: 160 1; insulation material: polyurethane; insulation thickness: 55 mm; exchanger type: jacket; heater power: 4 kW. Only the systems that are consistent with the above list will be selected. The above process will be repeated for the energy characteristics group and finally the output screen of Fig. 9 will appear indicating the optimal proposed system among those incorporated up to now to the expert system.
S. Panteliou et al.
FIRST LEVEL QUESTION=YES
OPENDATABASE FILE FOR SECOND LEVEL ATTRIBUTES
??
cr 3UTE
I
USER’S INPUT
hb
LOAD MEMBER ATTRIBUTE
NEXT MEMBER
No
Fig. 7. Flow chart of the repetitive process for SDHW selection.
7. CONCLUSIONS
According to the previous analysis and the corresponding application, the following major remarks can be stated. The adoption of expert system techniques to help the design of SDHW systems seems to be promising enough since it enables the combination of conventional design methods and procedures with design rules that incorporate the human expertise on the field. This combination configures a unique design tool that can accelerate, improve and optimize the quality of the final design. The choice of class objects and frame formalism as a basis for the development of the current expert system has been proven to be the optimal
path for representing both qualitative and quantitative characteristics of the SDHW systems. Moreover, the use of the conventional “IFTHEN” form for the knowledge base was found to be the simplest way to capture the expert design knowledge. In order to minimize the time required for the initial data input and to make the process more user friendly, the query process includes two levels of questions. Also the program guides the user continuously by giving them reports of the available choices, avoiding the input of non-acceptable values. In this way the user can configure a desirable design scheme, the characteristics of which, in any case, will be extracted from the already available SDHW systems.
Use of expert systems
for the selection
and the design of solar domestic
SET 1: COLLECTOR Define Define Define Define Define
the the the the the
hot water systems
7
INFORMATION
collector type: number of collectors: aperture area: number of pipes: pipe diameter:
Fig. 8. A typical input form concerning
information about a new collector enrichment process.
Fig. 9. Example
of an optimal
The program also contains a dynamic database. The user can enrich it by inserting any new SDHW system he desires, under the only restriction that he uses the specific enrichment procedure of the program. According to this procedure all the information obtained from the user, as well as intermediate conclusions inferred during the reasoning process, will be used during each execution of the program, but will be lost when the execution is terminated. It is very simple to add new attributes. The user can add new constraints for the selection part, by adding new members to class object questions, and the appropriate data files to the database. The program code is relatively small, since the member creation process takes place during runtime and the data for the available SDHW systems are kept in a separate database. Finally the aforementioned application of expert systems to the design of SDHW systems is only a preliminary proposal which is indicative of the possible extension of the case. In a
proposed
as it appears
during the database
system
future extension, the system can comprise some additional features, i.e. a learning-by-examples facility, as well as reasoning under uncertainty which will bring it closer to real-world situations and increase its reliability. Acknowledgements-The financial contribution of ITA (Institute for Technological Applications), branch of GPC (Greek Productivity Center), is greatly acknowledged.
REFERENCES Aranovitch E., Gilhaert G., Gillett W. B. and Bates J. E. (1989) Recommendations for performance and durability tests of solar collectors and water heating systems. ELOT (Greek Standard Organization) 879, Solar energy, domestic hot water systems, procedure for the estimation of the system’s efficiency and prediction of the annual energy consumption. Gillett W. B. and Moon J. E. (1985) Solar Collectors. Test Methods und Design Guidelines. D. Reidel Publishing Company, for the Commission of the European Communities. Jackson P. (1990) Introduction to Expert Systems, pp. 3-5. Addison Wesley Publishing Company.
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S. Panteliou ef al.
LEONARDO. Creative Logic Ltd. Pafelias T. ( 1991) Domestic hot water systems, measurements according to ELOT 879, summary of results and conclusions, Vol. A, CRES (Center of Renewable Energy Sources) Athens. Pham D. T. (1988) Expert Systems in Engineering. IFS Publications, Springer, Berlin.
Rich E. and Knight K. (1991) Artificial Intelligence. McGraw-Hill, New York. Winstanley G. (Ed.) (1991) Artificial Intelligence in Engineering. John Wiley & Sons, Chichester.