Advertisement

Review of Collective Intelligence Used in Energy Applications

  • Gülgün Kayakutlu
  • Secil Ercan
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 149)

Abstract

Low-Carbon Economy policies drive Europe for an integrated approach for utility consumption and management; number of integrated distribution companies are increasing. This new trend will soon cause the need for group decisions, collective intelligence approach to the energy industry. This study aims to review the collective intelligence concepts and methods to give a summary of collective intelligence use in energy applications. It can be considered as a foundation for the future of collective intelligence in the energy industry.

References

  1. Aghajani, G. R., Shayanfar, H. A., & Shayeghi, H. (2017). Demand side management in a smart micro-grid in the presence of renewable generation and demand response. Energy, 126, 622–637.CrossRefGoogle Scholar
  2. Assareh, E., Behrang, M. A., Assari, M. R., & Ghanbarzadeh, A. (2010). Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. Energy, 35(12), 5223–5229. Available from: http://dx.doi.org/10.1016/j.energy.2010.07.043.
  3. Bale, C. S. E., Varga, L., & Foxon, T. J. (2015, January 21). Energy and complexity: New ways forward. Appl Energy, 138, 150–159. Available from: http://www.sciencedirect.com/science/article/pii/S0306261914011076.
  4. Bitam, S., Mellouk, A., & Zeadally, S. (2013). HyBR: A hybrid bio-inspired bee swarm routing protocol for safety applications in Vehicular Ad hoc NETworks (VANETs). Journal of Systems Architecture, 59(10 PART B), 953–957.Google Scholar
  5. Bonabeau, E. (2009). Decisions 2.0: The power of collective intelligence. MIT Sloan Management Review, 50(2), 45–52.Google Scholar
  6. Bugała, A., Zaborowicz, M., Boniecki, P., Janczak, D., Koszela, K., Czekała, W., et al. (2018). Short-term forecast of generation of electric energy in photovoltaic systems. Renewable and Sustainable Energy Reviews, 81, 306–312.CrossRefGoogle Scholar
  7. Burke, M. J., & Stephens, J. C. (2017). Political power and renewable energy futures: A critical review. Energy Research & Social Science.Google Scholar
  8. Cardenas, J. A., Gemoets, L., Ablanedo Rosas, J. H., & Sarfi, R. (2014). A literature survey on smart grid distribution: An analytical approach. Journal of Cleaner Production, 65, 202–216.CrossRefGoogle Scholar
  9. Chaouachi, A., Kamel, R. M., Andoulsi, R., & Nagasaka, K. (2013). Multiobjective intelligent energy management for a microgrid. IEEE Transactions on Industrial Electronics, 60(4), 1688–1699.CrossRefGoogle Scholar
  10. Chu, S-C., Tsai, P., & Pan, J-S. (2006). Cat swarm optimization. In Pacific Rim International Conference on Artificial Intelligence (pp. 854–858). Available from: http://link.springer.com/10.1007/978-3-540-36668-3_94.
  11. Conrad, M. (1987). Rapprochement of artificial intelligence and dynamics. European Journal of Operational Research, 30(3), 280–290.CrossRefGoogle Scholar
  12. Cross, R. L., Jr., & Israelit, S. B. (2000). Strategic learning in a knowledge economy : Individual, collective and organizational learning process. In Resources for the knowledge based economy series (Vol. xviii, p. 348).Google Scholar
  13. Dorigo, M., Birattari, M., & Stützle, T. (2006) Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28–39. Available from: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4129846.
  14. Dorigo, M., & Di Caro, G. (1999). The ant colony optimization meta-heuristic. New Ideas Optimization, 2, 11–32. Available from: http://portal.acm.org/citation.cfm?id=329055.329062.
  15. Dudek, G. (2015). Pattern similarity-based methods for short-term load forecasting—Part 1: Principles. Applied Soft Computing, 37, 277–287.CrossRefGoogle Scholar
  16. Elia, G., Margherita, A., & Vella, G., et al. (2014). A conceptual model to design a collective intelligence system supporting technology entrepreneurship. In: C. Vivas, & P. Sequeira (Eds.), Proceedings of the 15th European Conference on Knowledge Management (ECKM 2014) (pp. 297–305).Google Scholar
  17. Faia, R., Pinto, T., Abrishambaf, O., Fernandes, F., Vale, Z., & Corchado, J. M. (2017). Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management. Energy and Buildings, 155.Google Scholar
  18. Favuzza, S., Graditi, G., Ippolito, M. G., & Sanseverino, E. R. (2007). Optimal electrical distribution systems reinforcement planning using gas micro turbines by dynamic ant colony search algorithm. IEEE Transactions on Power Systems, 22(2), 580–587.CrossRefGoogle Scholar
  19. Fisch, D., Jänicke, M., Kalkowski, E., & Sick, B. (2012). Learning from others: Exchange of classification rules in intelligent distributed systems. Artificial Intelligence, 187–188, 90–114.CrossRefGoogle Scholar
  20. García, T. R., Cancelas, N. G., & Soler-Flores, F. (2014). The artificial neural networks to obtain port planning parameters. Procedia-Social and Behavioral Sciences, 162, 168–177. Available from: http://linkinghub.elsevier.com/retrieve/pii/S1877042814062983.
  21. Gavrilets, S. (2015). Collective action and the collaborative brain. Journal of the Royal Society Interface, 12(102), 20141067–20141067. Available from: http://rsif.royalsocietypublishing.org/cgi/doi/10.1098/rsif.2014.1067.
  22. Georges, P., Nietvelt, K., de Taisne, B., Stenqvist, A., & Schiavo, M. (2017). European integrated utilities in 2017: Rebenchmarking the sector. Standard & Poor’s Financial Services LLC. Available from: https://www.spratings.com/documents/20184/1481001/European+Integrated+Utilities+In+2017/a5a59071-259b-4566-a0e2-abfd1652d9fb.
  23. Ghanbari, A., Kazemi, S. M. R., Mehmanpazir, F., & Nakhostin, M. M. (2013). A cooperative ant colony optimization-genetic algorithm approach for construction of energy demand forecasting knowledge-based expert systems. Knowledge-Based Systems, 39, 194–206. Available from: http://dx.doi.org/10.1016/j.knosys.2012.10.017.
  24. González, J. S., Burgos Payán, M., & Riquelme Santos, J. M. (2018). Optimal design of neighbouring offshore wind farms: A co-evolutionary approach. Applied Energy, 209, 140–152.CrossRefGoogle Scholar
  25. He, Y., Jiao, J., Chen, Q., Ge, S., Chang, Y., & Xu, Y. (2017). Urban long term electricity demand forecast method based on system dynamics of the new economic normal: The case of Tianjin. Energy, 133, 9–22.CrossRefGoogle Scholar
  26. Huo, D., Le Blond, S., Gu, C., Wei, W., & Yu, D. (2018). Optimal operation of interconnected energy hubs by using decomposed hybrid particle swarm and interior-point approach. International Journal of Electrical Power & Energy Systems, 95, 36–46.CrossRefGoogle Scholar
  27. Ishida, T., Murakami, Y., Tsunokawa, E., Kubota, Y., & Sornlertlamvanich, V. (2011). Federated operation model for service grids. Cognitive Technologies, 279–298.Google Scholar
  28. Jalali, M., Zare, K., & Seyedi, H. (2017). Strategic decision-making of distribution network operator with multi-microgrids considering demand response program. Energy, 141, 1059–1071.CrossRefGoogle Scholar
  29. Jin, M., Feng, W., Marnay, C., & Spanos, C. (2017). Microgrid to enable optimal distributed energy retail and end-user demand response. Applied Energy. Google Scholar
  30. Kapetanios, E. (2008). Quo Vadis computer science: From turing to personal computer, personal content and collective intelligence. Data & Knowledge Engineering, 67(2), 286–292.MathSciNetCrossRefGoogle Scholar
  31. Kaplan, C. A. (2001). Collective intelligence: A new approach to stock price forecasting. In 2001 IEEE International Conference on Systems, Man and Cybernetics e-Systems and e-Man for Cybernetics in Cyberspace (CatNo01CH37236) (Vol. 5, pp. 2893–2898).Google Scholar
  32. Karaboga, D., & Akay, B. (2007). Artificial Bee Colony (ABC) Algorithm on training artificial neural networks. In 2007 IEEE 15th Signal Processing and Communications Applications (pp. 1–4). Available from: http://ieeexplore.ieee.org/document/4298679/.
  33. Karadede, Y., Ozdemir, G., & Aydemir, E. (2017). Breeder hybrid algorithm approach for natural gas demand forecasting model. Energy, 141, 1269–1284.CrossRefGoogle Scholar
  34. Kayakutlu, G., & Mercier-Laurent, E. (2017). Intelligence in energy. London: ISTE-Elsevier Ltd.CrossRefGoogle Scholar
  35. Kefayat, M., Lashkar Ara, A., & Nabavi Niaki, S. A. (2015, March 28). A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources. Energy Conversion and Management, 2, 149–161. Available from: http://www.sciencedirect.com/science/article/pii/S0196890414010760.
  36. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In 1995 Proceedings, IEEE International Conference on Neural Networks (Vol. 4, pp. 1942–1948).Google Scholar
  37. Khodja, F., Younes, M., Laouer, M., Kherfane, R. L., Kherfane, N. (2014). A new approach ACO for solving the compromise economic and emission with the wind energy. Energy Procedia, 893–906.Google Scholar
  38. Kitamura, S., Mori, K., Shindo, S., & Izui, Y. (2006). Modified multiobjective particle swarm optimization method and its application to energy management system for factories. Electrical Engineering in Japan, 156(4), 33–42. Available from: http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=21611108&lang=tr&site=ehost-live.
  39. Korsvold, T, Madsen, B-E., Bremdal, B., Herbert, M. C., Nystad, E., & Danielsen, J. E., et al. (2010). Creating an intelligent energy organization through collective learning. In SPE Intelligent Energy Conference & Exhibition (pp. 892–923). Available from: http://www.scopus.com/inward/record.url?eid=2-s2.0-77953993993&partnerID=40&md5=abf9e61bb1848d9d215ce144eb10f513.
  40. Koubaa, R., & Krichen, L. (2017). Double layer metaheuristic based energy management strategy for a fuel cell/ultra-capacitor hybrid electric vehicle. Energy, 133, 1079–1093.CrossRefGoogle Scholar
  41. Król, D., & Lopes, H. S. (2012). Nature-inspired collective intelligence in theory and practice. Information Sciences, 182(1), 1–2.MathSciNetCrossRefGoogle Scholar
  42. Kumar, S., & Chaturvedi, D. K. (2013). Optimal power flow solution using fuzzy evolutionary and swarm optimization. International Journal of Electrical Power & Energy Systems, 47, 416–423. Available from: http://www.sciencedirect.com/science/article/pii/S014206151200662X.
  43. Lahiani, A., Miloudi, A., Benkraiem, R., & Shahbaz, M. (2017). Another look on the relationships between oil prices and energy prices. Energy Policy, 102, 318–331.CrossRefGoogle Scholar
  44. Lazaric, N., Mangolte, P. A., & Massué, M. L. (2003). Articulation and codification of collective know-how in the steel industry: Evidence from blast furnace control in France. Research Policy, 32(10), 1829–1847.CrossRefGoogle Scholar
  45. Li, Z., Xie, Z., & Qin, J. (2005). Application of ant colony algorithms in optimized design of gas transmission pipelines. Tianranqi Gongye/Natural Gas Ind., 25(9), 104–119.Google Scholar
  46. Liu, X., Jiang, T., & Ma, F. (2013). Collective dynamics in knowledge networks: Emerging trends analysis. Journal of Informetrics, 7(2), 425–438.CrossRefGoogle Scholar
  47. Lo, C. C., Tsai, S. H., & Lin, B. S. (2016). Economic dispatch of chiller plant by improved ripple bee swarm optimization algorithm for saving energy. Applied Thermal Engineering, 100, 1140–1148.CrossRefGoogle Scholar
  48. Ma, W., Fang, S., Liu, G., & Zhou, R. (2017). Modeling of district load forecasting for distributed energy system. Applied Energy, 181–205.Google Scholar
  49. Maleszka, M., & Nguyen, N. T. (2015). Integration computing and collective intelligence. Expert Systems with Applications, 42(1), 332–340.CrossRefGoogle Scholar
  50. Mamatzakis, E., & Koutsomanoli-Filippaki, A. (2014). Testing the rationality of DOE’s energy price forecasts under asymmetric loss preferences. Energy Policy, 68, 567–575.CrossRefGoogle Scholar
  51. Marzband, M., Yousefnejad, E., Sumper, A., & Domínguez-García, J. L. (2016). Real time experimental implementation of optimum energy management system in standalone Microgrid by using multi-layer ant colony optimization. International Journal of Electrical Power & Energy Systems, 75, 265–274.CrossRefGoogle Scholar
  52. Melichar, M. (2016). Energy price shocks and economic activity: Which energy price series should we be using? Energy Economics, 54, 431–443.CrossRefGoogle Scholar
  53. Merali, Y. (2000). Individual and collective congruence in the knowledge management process. The Journal of Strategic Information Systems, 9(2–3), 213–234. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0963868700000445.
  54. Mezian, R., Boufala, S., Amara, M., & Amara, H. (2015). Cat swarm algorithm constructive method for hybrid solar gas power system reconfiguration. In: 3rd International Renewable and Sustainable Energy Conference (IRSEC) (pp. 1–7).Google Scholar
  55. Moffett, M. W. (2010). Adventures among ants: A global Safari with a cast of trillions. (1–280 pp). Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987834827&partnerID=40&md5=5d907b41e3ab02966b2b1695c30e04d7.
  56. Nemoz, S. (2013). Smart campus: Recent advances and future challenges for action research on territorial sustainability. Implementing Campus Greening Initiatives, 313–323.Google Scholar
  57. Palensky, P., & Dietrich, D. (2011). Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE Transactions on Industrial Informatics, 7(3), 381–388.CrossRefGoogle Scholar
  58. Paul, S., Haseman, W. D., & Ramamurthy, K. (2004). Collective memory support and cognitive-conflict group decision-making: An experimental investigation. Decision Support Systems, 36(3), 261–281.CrossRefGoogle Scholar
  59. Penalva, J. M. (2006). Intelligence collective. Paris: Mines.Google Scholar
  60. Pi, D., Liu, J., & Qin, X. (2010). A grey prediction approach to forecasting energy demand in China. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 32(16), 1517–1528.CrossRefGoogle Scholar
  61. Rahimian, M., Cardoso-Llach, D., & Iulo, L. D. (2015). Participatory energy management in building networks. Sustainable Human-Building Ecosystems, 27–35.Google Scholar
  62. Rahmani, R., Yusof, R., Seyedmahmoudian, M., & Mekhilef, S. (2013). Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting. Journal of Wind Engineering and Industrial Aerodynamics, 123, 163–170.CrossRefGoogle Scholar
  63. Rajasekhar, A., Lynn, N., Das, S., & Suganthan, P. N. (2017) Computing with the collective intelligence of honey bees—A survey. Swarm and Evolutionary Computation, 25–48.Google Scholar
  64. Raza, M. Q., & Khosravi, A. (2015). A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable and Sustainable Energy Reviews., 50, 1352–1372.CrossRefGoogle Scholar
  65. Redlarski, G., Krawczuk, M., Kupczyk, A., Piechocki, J., Ambroziak, D., & Palkowski, A. (2017). Swarm-assisted investment planning of a bioethanol plant. Polish Journal of Environmental Studies, 26(3), 1203–1214.CrossRefGoogle Scholar
  66. Rhein, A., Balzer, G., & Renz, P. (2017). Reliability-based improvement of life-cycle maintenance and replacement strategies in transmission systems using ant colony optimization. In 2017 6th International Youth Conference on Energy (IYCE).Google Scholar
  67. Robinson, D. G. (2005). Reliability analysis of bulk power systems using swarm intelligence Year: 2005. In Annual Reliability and Maintainability Symposium (pp. 96–102).Google Scholar
  68. Saba, D., Laallam, F. Z., Hadidi, A. E., & Berbaoui, B. (2015). Contribution to the management of energy in the systems multi renewable sources with energy by the application of the multi agents systems “MAS.” Energy Procedia, 616–623.Google Scholar
  69. Selvakumar, K., Vijayakumar, K., & Boopathi, C. S. (2017). Demand response unit commitment problem solution for maximizing generating companies’ profit. Energies, 10(10).Google Scholar
  70. Sharafi, M., & ElMekkawy, T. Y. (2015). Stochastic optimization of hybrid renewable energy systems using sampling average method. Renewable and Sustainable Energy Reviews, 52, 1668–1679.CrossRefGoogle Scholar
  71. Sisodia, G. S., Soares, I., Banerji, S., & Van Den Poel, D. (2015). The status of energy price modelling and its relevance to marketing in emerging economies. Energy Procedia, 500–505.Google Scholar
  72. Sonmez, M., Akgüngör, A. P., & Bektaş, S. (2017). Estimating transportation energy demand in Turkey using the artificial bee colony algorithm. Energy, 122, 301–310.CrossRefGoogle Scholar
  73. Suhane, P., Rangnekar, S., Mittal, A., & Khare, A. (2016). Sizing and performance analysis of standalone wind-photovoltaic based hybrid energy system using ant colony optimisation. IET Renewable Power Generation, 10(7), 964–972. Available from: http://ieeexplore.ieee.org/ielx7/4159946/7514393/07514408.pdf?tp=&arnumber=7514408&isnumber=7514393%5Cnhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7514408&source=tocalert&dld=aG90bWFpbC5jb20=.
  74. Su, Z., Wang, J., Lu, H., & Zhao, G. (2014). A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting. Energy Conversion and Management, 85, 443–452.CrossRefGoogle Scholar
  75. Szuba, T. (1999). A formal definition of the phenomenon of collective intelligence and its IQ measure. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (pp. 165–173).Google Scholar
  76. Tapscott, D., & Williams, A. D. (2008). Wikinomics: How mass collaboration changes everything. Journal of Information Technology and Politics, 5(2), 259–262. Available from: http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=35157924.
  77. Thomas, L. (1973). On various words. The New England Journal of Medicine, 289(19), 1024–1026.CrossRefGoogle Scholar
  78. Toksarı, M. D. (2009). Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey. Energy Policy, 2009(37), 1181–1187.CrossRefGoogle Scholar
  79. Toksari, M. D. (2016). A hybrid algorithm of Ant Colony Optimization (ACO) and Iterated Local Search (ILS) for estimating electricity domestic consumption: Case of Turkey. International Journal of Electrical Power & Energy Systems, 78, 776–782.CrossRefGoogle Scholar
  80. Tounsi, W., & Rais, H. (2018). A survey on technical threat intelligence in the age of sophisticated cyber attacks. Computers and Security., 72, 212–233.CrossRefGoogle Scholar
  81. Toyokawa, W., Kim, H. R., & Kameda, T. (2014). Human collective intelligence under dual exploration-exploitation dilemmas. PLoS One, 9(4), e95789.CrossRefGoogle Scholar
  82. Udayraj, Mulani, K., Talukdar, P., Das, A., & Alagirusamy, R. (2015). Performance analysis and feasibility study of ant colony optimization, particle swarm optimization and cuckoo search algorithms for inverse heat transfer problems. International Journal of Heat and Mass Transfer, 89, 359–378.Google Scholar
  83. Ünler, A. (2008). Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025. Energy Policy, 36(6), 1937–1944.CrossRefGoogle Scholar
  84. Watanabe, I. (2005). An ACO algorithm for service restoration in power distribution systems. In IEEE CEC 2005 Proceedings IEEE Congress on Evolutionary Computation (pp. 2864–2871).Google Scholar
  85. Xia, F., Zhao, X., Zhang, J., Ma, J., & Kong, X. (2014). BeeCup: A bio-inspired energy-efficient clustering protocol for mobile learning. Future Generation Computer Systems, 37, 449–460.CrossRefGoogle Scholar
  86. Xu, X., Mitra, J., Cai, N., & Mou, L. (2014). Planning of reliable microgrids in the presence of random and catastrophic events. International Transactions on Electrical Energy Systems, 24(8), 1151–1167.CrossRefGoogle Scholar
  87. Yanine, F. F., Caballero, F. I., Sauma, E. E., & Córdova, F. M. (2014). Building sustainable energy systems: Homeostatic control of grid-connected microgrids, as a means to reconcile power supply and energy demand response management. Renewable and Sustainable Energy Reviews., 40, 1168–1191.CrossRefGoogle Scholar
  88. Yegane, B. Y, Nakhai Kamalabadi, I., & Farughi, H. (2016). A non-linear integer bi-level programming model for competitive facility location of distribution centers. International Journal of Engineering-Transactions B: Applications, 29(8), 1131–1140. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984637702&partnerID=40&md5=2cfe1e1faca06a0dedd3440ab0cfb226.
  89. Zahraee, S. M., Khalaji Assadi, M., & Saidur, R. (2016). Application of artificial intelligence methods for hybrid energy system optimization. Renewable and Sustainable Energy Reviews, 66, 617–630. Available from: http://dx.doi.org/10.1016/j.rser.2016.08.028.
  90. Zak, M., & Zak, A. P. (1994). Unpredictable dynamics and collective brain. Computers & Mathematics with Applications, 27(9110), 185–197.CrossRefzbMATHGoogle Scholar
  91. Zhang, H., Li, Z., Qu, Z., & Lewis, F. L. (2015). On constructing Lyapunov functions for multi-agent systems. Automatica, 58, 39–42.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ITU Ayazaga Campus, Energy InstituteSariyer, İstanbulTurkey
  2. 2.University of Reims Champagne ArdennesGrand EstFrance

Personalised recommendations