Hybrid Decision-Making System in Dispersed and Distributed Generation Management

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1 Association for Information Systems AIS Electronic Library (AISeL) AMCIS 2006 Proceedings Americas Conference on Information Systems (AMCIS) December 2006 in Dispersed and Distributed Generation Management Robert Kuc??ba Cz??stochowa University of Technology Leszek Kie??tyka Cz??stochowa University of Technology Follow this and additional works at: Recommended Citation Kuc??ba, Robert and Kie??tyka, Leszek, " in Dispersed and Distributed Generation Management" (2006). AMCIS 2006 Proceedings This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL). It has been accepted for inclusion in AMCIS 2006 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact

2 in Dispersed and Distributed Generation Management Robert Kuc ba Department of Management Information Systems Management Faculty Cz stochowa University of Technology Al. Armii Krajowej 19B Cz stochowa, Poland Leszek Kie tyka Department of Management Information Systems Management Faculty Cz stochowa University of Technology Al. Armii Krajowej 19B Cz stochowa, Poland ABSTRACT The basic aim of the present paper is to present a practical solution of an intelligent Business Class system of aiding decisionmaking in activity management processes of small energy sources functioning on the electricity market. The solution has been worked out by the authors of the paper. A hybrid approach combining SHELL applications and neural networks has been applied. This hybrid, together with expert systems, aid preprocessing of expert knowledge in prediction and decision generating processes in neural networks (MLP Multilayer Perceptrons and GRNN Generalized Regression Neural Network). The whole system functions on the basis of knowledge databases, developed using CAKE tools. Keywords Business Intelligence, dispersed and distributed generation, artificial intelligence tools. INTRODUCTION The factor supporting and uniting the contemporary energy providers is an efficient information system, based on the teleinformatic platform. An important task of this system is to coordinate decision-making processes both inside particular energy providers and on the open market of energy. The new forms of organizing enterprises for example virtual, network, self-learning organizations that are entering the electric energy area, require new solutions within the IT systems. Network services are becoming more popular and larger diversification of the individual participants needs is taking place on the electricity market. Classic relations of informatic systems often become insufficient for these frequently geographically dispersed and having dispersed strategic aims market participants. New requirements involve increased flexibility, autonomy and reactivity of the system to the changes taking place in the environment. Thus, the answer to the needs of the energy providers market in reference to IT are Business Intelligence Class systems. The research team run by the authors of the paper has been modeling and developing informatic systems supporting functioning of the electricity market (based on Business Intelligence Class systems) for many years. The authors are particularly interested in integrating small conjugated electricity sources (dispersed generation up to 1MW and distributed generation above 1MW) with the energetic systems, and what follows with the energy market (Kuc ba, 2003). Described in the paper individual modules of the hybrid decision-making system in dispersed and distributed generation management were developed in the course of the research project Self-learning organization model in the virtual management environment Research project report State Committee for Scientific Research, Nr 2H02D 02423, 2004 (Kuc ba, Kie tyka, Soko owski and Wrzalik, 2004). The present paper is an extension of the above mentioned project. The authors concentrate in it on knowledge creation and generating decisions and interaction with the environment by Business Intelligence Class systems on the basis of the created knowledge. In the case of the present paper they are the components of the presented hybrid (neural networks and expert systems). BUSINESS INTELLIGENCE CLASS SYSTEMS IN ACTIVITY MANAGEMENT OF SMALL CONJUGATED ENERGY SOURCES From the point of view of energy administration (Linthicum, 2001) Business Intelligence is an IT solution group comprising the areas of integrating e-business class systems with the domain applications using knowledge base and artificial intelligence 1417

3 tools. The point of departure for the present paper is the simplified diagram of the two-level Business Intelligence system structure presented in Figure 1. The first (external) level involves integration of the system of Information Exchange on the Electricity Market (IEEM) of the central Transfer Network Operator (TNO) node with IEEM/UR applications of the local user (10). The IEEM systems are meant for the exchange of trade and technical information for the needs of planning and accounting. The IEEM system architecture is of an open nature because it must integrate various informatic solutions determined by proper standards. The IEEM system includes Data Warehouse (DW), where all the documents subject to exchange and information concerning the functioning of the energy market are gathered. Particularly important in the published information is data and information concerning the forced reliability production (8, 9). Figure 1 The two-level Business Intelligence diagram on the wholesale electric energy market. Source: (8, 9) The program IEEM module comprises two parts: the central one and the local one. The central one, located in the Transfer Network Operator (TNO), is realized on the base of the central knowledge base created in Data Warehousing technology. It gathers all the documents subject to exchange and data concerning the functioning of the energy market. The second level of the presented systems (the internal one) involves integration of the IEEM/UR applications with the domain application of the local user node. The integration among individual domain applications must be mentioned here. They include: Enterprise Resources Planning II (ERP II), Supply Chain Management (SCM) systems and Partner Relationship Management (PRM). The above-mentioned systems have to be compatible and integrated with IEEM/UR systems (in case of local users) and central IEEM (TNO). The SCM systems maintain flow of information, materials, products and services within the energy market participants as well as in their environment. They integrate logistic operations with the procedures of the business partners. The contemporary systems may maintain the relationship between the participants of the individual energy market and the business partners. These systems called Partner Relationship Management systems (PRM) are based on Customer Relationship Management (CRM) applications (Dyche, 2000). An important factor integrating the above mentioned systems (Figure 1) on the Business Intelligence platform is the program compatibility of the individual applications on all the levels of integration. For that reason the messages are created in the homogenous Java environment. Central and local data warehouses operate in the ORACLE environment. Data warehousing is used by the integrated ERP class systems, PRM and SCM systems. In case of communication layer integration with the external systems a shared application environment VPN Client is used. Research, simulations and attempts to adapt systems based on knowledge, artificial intelligence, Multiagent Systems (MAS) and Business Intelligence Class systems are being carried out all over the world. That is why the authors of this paper made 1418

4 an attempt to implement these tools. Integration of the small energy sources and their management using the proposed tools is the novelty introduced in their research. These small energy sources may soon become an alternative to the large energy providers. This is possible thanks to their physical and logical integration as well as integrated management system. Due to their geographical distribution, large amount of dispersed information and substantial fragmentation the authors concentrated on the research described in the present paper. KNOWLEDGE COMPONENTS IMPLEMENTED IN THE EXPERT MODULE At the first stage of the research, on the basis of the conducted survey and interviews conducted in the chosen power enterprises in the electricity and heat production area, the key competences of the dispersed and distributed generation operating in the energy system were determined. On the basis of the determined core competences of dispersed and distributed generation sources knowledge components were determined. They are essential to generate initial knowledge bases in the proposed hybrid system which is based on artificial intelligence. They include: introducing if need be current changes in the work program of small power production units, elaborating work schedule of individual units taking into consideration changes caused by planned and emergency decommissioning of machines, securing continuation of electricity production, power machines activity security and maintaining proper parameters of the produced electricity in reference to the energy market requirements. Knowledge components were classified in the two separated knowledge bases the wholesale energy market knowledge and in respect to the heat market specificity the local heat market knowledge base. The simplified structure of the input knowledge bases, directly integrated with the expert module, being an integral part of the Hybrid decision-making system in dispersed and distributed generation management is presented in Figure 2. Figure 2. The Shell Expert Module knowledge base structure. Source: own analysis The presented in the above described structure two first layers: the database layer and the knowledge base layer constitute the operational part of the proposed hybrid due to their specificity and functionality. In accordance with the classic IT system 1419

5 convention the operational part ensures, among other things: data gathering, data search, data sorting, formulating queries, data mining, as well as generating and gathering knowledge thanks to the possibility of formulating conditions, rules, and facts. This part is fully integrated with the application part described in chapter 3. THE STRUCTURE OF THE HYBRID DECISION-MAKING SYSTEM The proposed Hybrid decision-making system in dispersed and distributed generation management is of a modular structure and basically it consists of the two parts: the operational one and the application one. Particular modules in the system may be activated or deactivated depending on the user preferences and the nature of the research conducted (Kie tyka, Kuc ba and Soko owski, 2004). Where: WEM - Wholesale Electricity Market; LHM Local Heat Market. Figure 3. Flow chart of the Hybrid decision-making system in dispersed and distributed generation management. Source: own analysis The following main modules (Figures 2 and 3) were defined within the structure of the proposed model: A. In the operational part: The Data acquisition module. The Creating rules and formulas module knowledge generator. B. In the analytic part: The Shell expert module together with pre-processing module. The Input set of the Intelligent System of Decision Aiding. The Intelligent module of decision aiding. The Resultant/verifying module. The mechanisms of data updating and aggreging were defined in the Data Acquisition Module in the two separated sets integrated with the partial databases: data on the Wholesale Electricity Market and data on the Local Heat Market (Figure 2). 1420

6 The Creating rules and formulas module knowledge generator was elaborated on the basis of CAKE - computer tools aiding knowledge engineering. The choice of these tools was not a coincidence. They facilitate realization of domain processes of expert systems frame applications. The two separated groups of rules and facts corresponding to the elaborated knowledge bases were worked out in the project. They were worked out on the basis of the specialized knowledge base editor, automatic source text generation of the knowledge base, which CASE tools offer. In the database on the wholesale electricity market there was a simple set of rules based on IF index elaborated. These rules classify inserted attributes (input data on the wholesale electricity market) determining their order of magnitude, electricity shortages on the market, balance on the market, energy surpluses on the market. A database is elaborated in the course of realization. Accounting Prices of Deviations (APD) are classified in this base as well as energy price on the Energy Exchange (Eep) in twenty-four-hour/hour turn. A set of rules and facts that enable classifying heat receivers according to the scale of their demand for this type of energy was elaborated in the knowledge base on the local heat market (Example 1). The above classification in the processes preceding neural networks teaching is vital as it increases the accuracy of their prediction and classification. On the basis of the literature study and the conducted research it must be stressed here that inserting accumulated input variables without conducting classification in accordance with the determined rules causes generalization of the generated results. A generalized teaching set causes significant dispersion of the predicted or classified values as neural network adjusts itself to the general information. Serious errors: AvgError (testing) and RMSError (teaching) received at the initial stage of the research, without applying knowledge bases and expert system also proved this fact (Duch, 2000). Example 1 The example of classifying receivers according to the cubature of the heated places. The Shell expert module is a structural element of the proposed Hybrid s analytic part. There is information as well as knowledge components generated in this module, which are in turn implemented by neural network in the prediction and classification process. The input set variables implemented in neural networks are generated in the expert module on the basis of formulas and rules defined in the knowledge generator module. The Input set of the Intelligent System of Decision Aiding defined in the expert module and pre-processed by the preprocessing procedures constitutes the archetype for the following sets: teaching, testing and validating ones of neural networks. The teaching set is used in the process of neural network teaching, the testing set and the validating set support activity verification processes of the sub-optimum network architecture. The Intelligent Module of Decision Aiding constitutes the executable element of the Hybrid decision-making system in dispersed and distributed generation management. The optimum architectures of neural networks were defined in this module. The set of neural networks was defined preserving their structural diversity. Within the confines of the research, among other things, Radial Basis Function Network architectures, Generalized Regression Neural Network, Multilayer Perceptrons unidirectional multi-layer networks and Linear Networks were defined and studied, in the prediction process of the researched phenomenon. The results of the prediction are compared in the Resultant/Verifying Module, in the two separated output bases. They are: the electricity demand on the Wholesale Electricity Market and the heat demand on the Local Heat Market. These processes of core competence realization of particular generation sources are defined on the basis of the determined variables (chapter 2). There are also accuracies generated in the Resultant/Verifying Module. Universal measures of the prediction quality were used here. They include ex post errors, prediction accuracy index, average square error of prediction in the prediction verification section. Example 2 Generated in the Resultant/Verifying Module (architecture selection process and weight coefficients of neural networks) sample results of the heat prediction implemented in the decision-making process of core competence realization (9, 11). 1421

7 As it can be observed in the above example, the hybrid may be successfully applied in the decision aiding of small energy sources activity management in reference to a changeable environment mechanisms of energy market functioning. High accuracy of the prediction generated in the Resultant/Verifying Module is one of the features supporting this conclusion. CONCLUSION The choice of the hybrid system, dedicated to realize the appointed tasks was not a coincidence. It was the result of the former research conducted by the authors (9). The first research concerned implementation of separated systems based respectively on: expert systems and neural networks (7) in the decision-making process (prediction, classification). However, the Hybrid decision-making system in dispersed and distributed generation management has been elaborated in order to increase the accuracy of prediction classification. On the basis of the received results it can be stated that pre-processing classification and at the same time adapting by Shell systems input sets in the Intelligent Module of Decision Aiding (neural networks) increases the quality of the decision-making system (Example 2). In accordance with the determined in the Abstract range of the paper and limited work structure, it has been decided to present in the paper only the aims of the realized project and its functioning structure. Due to the large extent of the research area only examples of the realized research are presented. The authors would like to thank all the reviewers for their valuable suggestions that helped to elaborate the final version of the paper. They will surely be very useful in further research in the discussed area. The research financed from the funds of The State Committee for Scientific Research in the years as the research 1H02D0727 REFERENCES 1. Duch, W., Korbicz, J., Rutkowski L. and Tadusiewicz R. (2000) Biocybernetics and Biomedical Engineering. Neural Networks, Vol. 6, Warszawa, AOW Exit. 2. Dyche, J. (2000) E-data, Turning Data into Information with Data Warehousing, Addison-Wesley, Boston. 3. Kuc ba, R. (2003) A virtual power plant basing on dispersed and distributed generation, in Proceedings of the International Association for Development of the Information Society International Conference e-society 2003, Volume I, Lisbon, Portugal, s Kie tyka, L., Kuc ba, R. and Soko owski, A. (2004) Application of Neural Network Topologies in the Intelligent Heat Use Prediction System, in Rutkowski, L., Siekmann, J., Tadeusiewicz, R., Zadeh, L.A. Artifical Intelligence and Soft Computing ICAISC 2004, Proceedings of the 7 th International Conference Zakopane, Poland, Springer-Verlag Berlin Heidelberg New York, s Kuc ba, R., Kie tyka, L., Soko owski, A. and Wrzalik, A. (2004) A self-learning organization model in the virtual management environment Research project report State Committee for Scientific Research, Nr 2H02D Linthicum, D. S. (2001) B2B Application Integration: e-business-enable Your Enterprise, Addison-Wesley, Boston