Monday , June 18 2018

Renewable Energy Decision Support Systems:
The Challenge of Data Integration

Ioana Andreea STANESCU1*, Veronica STEFAN2, Gabriela NEAGU3, Carmen Elena CIRNU4

1 Advanced Technology Systems,
1, Tineretului Str., Târgoviște, 130029, Romania
2 Valahia University of Târgovişte,
2, Carol Bd., Târgoviște, 130024, Romania
3 Research Institute for Quality of Life,
13 Calea 13 Septembrie, Bucharest, 050711, Romania

4 I C I Bucharest
(National Institute for R & D in Informatics)

8-10 Averescu Blvd.
011455 Bucharest 1, Romania

Corresponding author

Abstract: The practical benefits of a Decision Support System rely heavily on the system’s ability to efficiently aggregate and manage large volumes of data, information, and knowledge from heterogeneous sources. The authors approach this challenge and explore the concept of flexible software architectures that support highly customizable decision environments at end-user level. The paper proposes a mechanism applied in the Renewable Energy filed that enables end-users to visually describe data and knowledge source structures using a set of data types and semantic annotations, and to integrate them into the DSS logic without intervention from system developers.

Keywords: Dynamic software architecture, self-service, INDESEN.

>>Full text
Ioana Andreea STANESCU, Veronica STEFAN, Gabriela NEAGU, Carmen Elena CIRNU, Renewable Energy Decision Support Systems: The Challenge of Data Integration, Studies in Informatics and Control, ISSN 1220-1766, vol. 24 (2), pp. 191-200, 2015.

  1. Introduction

Building upon [1], [2] and the “Europe 2020: A strategy for smart, sustainable and inclusive growth”, the Future Internet Enterprise Systems (FInES) Research Roadmap 2025 [3] envisioned the evolution of key technological areas focusing on networking, knowledge, application, computation and storage technologies, stipulating that:

  • The existing understanding of networking will be extended to accommodate advanced forms of collaboration, Interoperability Service Utility, and security;
  • High performance content networks will be capable to extensively store, link, integrate, and distribute data and knowledge, coming from any possible entity and source;
  • Future computing and storage capacities will be based on smart proactive interconnected objects, able to collaborate spontaneously to create more complex computational entities.

The authors consider such foreseen evolvements and related research [4], [5] from their potential to improve the end-user experiences by increased their independence from information system developers and by enhancing the outcomes of decision processes in terms of speed, efficiency, cost, quality and security.

The paper explores opportunities for Decision Support Systems (DSS) customization at end-user level within the context of the INDESEN project, by employing dynamic software architectures to enable accommodation of new data and knowledge sources, as well as the adaptation of the system logic, without support from DSS developers.

This self-service approach has even higher relevance in decision support, where the efficiency of the decision making processes depends extensively on the timely availability, accuracy, and quality of data and knowledge sources.

Currently, when data and knowledge sources used by a DSS become obsolete or their quality decreases, or when new sources become available, end-users cannot update the sources employed by the system without support from developers. This creates a major drawback, which in domains such as Renewable Energy (RE), which employ a large variety of equipment and services, is even more significant.

Increased market dynamics require new abilities to aggregate and process data and knowledge sources. For decision makers it is crucial to be able to easily integrate new data and knowledge generated and collected in heterogeneous environments, by a large number of entities.

To address this issue, DSS developers have sought solutions based on data integration and interoperability practices. However, how to design DSSs with a high capability to use data from a large number of sources remains an open issue. Moreover, even if these solutions have aimed to enable access to a considerably large number of data sources, DSS customization and access to new data sources cannot be implemented without assistance from system developers.

If for larger enterprises that have their own IT department or IT experts this is an issue that can be overcome easier, for micro, small and medium enterprises, the inability to adapt a DSS to their specific needs creates major inefficiencies, especially since RE parks are difficult to manage without monitoring and control systems.

This paper addresses these challenges and builds upon the premise that decision makers need a more personal and a direct control of the DSS [6].


Several data integration challenges are presented and issues specific for the RE field are discussed. The authors introduce a mechanism that facilitates data and knowledge integration at end-user level within the INDESEN System.


  1. FInES RESEARCH ROADMAP TASK FORCE. Enterprise Interoperability Research Roadmap, European Commission, 2008.
  2. FInES RESEARCH ROADMAP TASK FORCE. Future Internet Enterprise Systems (FInES) Research Roadmap, European Commission, 2010.
  3. FInES RESEARCH ROADMAP TASK FORCE. Future Internet Enterprise Systems (FInES) Research Roadmap 2025, European Commission, 2012.
  4. DACLIN, N., S. MALLEK, Capturing and Structuring Interoperability Requirements: A Framework for Interoperability Requirements, in Enterprise Interoperability VI. Interoperability for Agility, Resilience and Plasticity of Collaborations (Eds. MERTINS, K, BENABEN, F., POLER, R., BOURRIERES, J. P.), Springer, 2014.
  5. PETYCHAKIS, M., L. ALVERTIS, E. BILIRI, T. TSOUROPLIS, F. LAMPATHAKI, D. ASKOUNIS, Enterprise Collaboration Framework for Managing, Advancing and Unifying the Functionality of Multiple Cloud-Based Services with the Help of a Graph API, in Collaborative Systems for Smart Networked Environments (Eds. CAMARINHA-MATOS, L., AFSAR-MANESH, H.), Springer, 2014.
  6. ZARATÉ, P. Integrated and Strategic Advancements in Decision Making Support Systems, IGI Global, 2012.
  7. IVES, Z., A. HALEVY, A. DOAN, Principles of Data Integration, Elsevier Science, Morgan Kaufmann, Waltham, 2012.
  8. LOSHIN, D. Enterprise Knowledge Management, Morgan Kaufmann, San Diego, 2001.
  9. RONEN, T., E. PASHER, The Complete Guide to Knowledge Management: A Strategic Plan to Leverage Your Company’s Intellectual Capital, John Wiley & Sons, New Jersey, 2011.
  10. LYLES, M. A., M. EASTERBY-SMITH, Handbook of Organizational Learning and Knowledge Management, John Wiley & Sons, 2011.
  11. DELTOUR, F., L. PLE, C. SARGIS ROUSSEL, Knowledge Sharing in the Age of Web 2.0: A Social Capital Perspective. In Knowledge Management 2.0, Dudezert, A., Boughzala, I. (Ed.), IGI Global, 2011.
  12. SMIRNOV, A., T. LEVASHOVA, N. SHILOV, Knowledge Fusion Patterns for Context Aware Decision Support, In Encyclopedia of Information Science and Technology, Khosrow-Pour, M. (Ed.), IGI Global, 2014.
  13. POHL, J., Intelligent Software Systems in Historical Context, Phillips-Wren G. and L. Jain (eds.); Decision Support Systems in Agent-Based Intelligent Environments; Knowledge-Based Intelligent Engineering Systems Series; IOS Press, Amsterdam, The Netherlands, 2005, pp. 3-34.
  14. LIN, N., Applied Business Analytics: Integrating Business Process, Big Data, and Advanced Analytics, Pearson FT Press, 2014.
  15. DAVENPORT, T. H. The Complete Guide to Business Analytics, FT Press, 2012.
  16. ESCANDELL-MONTERO, P., J. VILA-FRANCES, J. MARTINEZ-MARTINEZ, M. MARTINEZ-SOBER, R. MAGDALENA-BENEDITO, Intelligent Data Analysis for Real-Life Applications, IGI Global, 2012.
  17. GREGG, B., Systems Performance: Enterprise and the Cloud, Prentice Hall, New Jersey, 2013.
  18. MAHMOOD, Z., T. ERL, R. PUTTINI, Cloud Computing: Concepts, Technology & Architecture, Pretince Hall, Westford, 2013.
  19. STALLMAN, R. M., Free Software, Free Society: The Selected Essays of Richard M. Stallman. Retrieved from: Ecosystem
  20. MARINESCU, D. C., Cloud Computing, Elsevier Science, Morgan Kaufmann, Waltham, 2013.
  21. BEDFORS, A., Energy Management System Concepts, In Olla, P. Global Sustainable Development and RE Systems. IGI Global, Hershey, 2012.
  22. VIARDOT, E. Encouraging the Development of RE: The Role of Cooperatives, In Global Sustainable Development and RE Systems, Olla, P. (Ed.), IGI Global, Hershey, 2012.
  23. STĂNESCU, I. A., A. STEFAN, F. G. FILIP, C. KITTL, T. LIM, Interoperability Scenarios in Serious Games Ecosystems: The Impact on FInES, 7th IFAC Conference on Manufacturing Modelling, Management, and Control, Volume # 7, | Part# 1, Elsevier, St. Petersburg, 2013,   pp. 1334-1339.
  24. SALAMEH, Z., RE System Design, Academic Press, Waltham, 2014.
  25. CORONA, C., A. ARRENDONDO, J. LOZANO, M. CASCALES, Soft Computing Applications for RE and Energy Efficiency, IGI Global,     Herchey, 2014.
  26. LUND, H. RE Systems, Academic Press, Waltham, 2014.
  27. da ROSA, A. V. Fundamentals of RE Processes, Academic Press, Oxford, 2013.