Abstract
Energy efficiency and occupant’s comfort are two primary concerns for evaluating the performance of a building control system. This paper introduces an optimization method based on integration of genetic algorithms for local optimization and agent-based approach for integration of the locally optimal solution into a global solution that is close to the global optimum. The approach is validated on a case study that models a cluster of eight buildings sharing resources of a district heating system.
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References
Heating and cooling no longer majority of u.s. home energy use. http://www.eia.gov/todayinenergy/detail.cfm?id=10271 (accessed: 2015–03-20)
Atthajariyakul, S., Leephakpreeda, T.: Real-time determination of optimal indoor-air condition for thermal comfort, air quality and efficient energy usage. Energy and Buildings 36(7), 720–733 (2004)
Bellifemine, F.L., Caire, G., Greenwood, D.: Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology). John Wiley & Sons (2007)
Djuric, N., Novakovic, V., Holst, J., Mitrovic, Z.: Optimization of energy consumption in buildings with hydronic heating systems considering thermal comfort by use of computer-based tools. Energy and Buildings 39(4), 471–477 (2007)
Ferreira, P.M., Ruano, A.E., Silva, S., Conceição, E.Z.E.: Neural networks based predictive control for thermal comfort and energy savings in public buildings. Energy and Buildings 55, 238–251 (2012)
Qiao, B., Liu, K., Guy, C.: A multi-agent system for building control. In: In proceedings of the IEEE International Conference on Intelligent Agent Technology, pp. 653–659, December 2006
Yang, R., Wang, L.: Optimal control strategy for hvac system in building energy management. In: 2012 IEEE PES Transmission and Distribution Conference and Exposition (T D), pp. 1–8 (2012)
Yang, R., Wang, L.: Development of multi-agent system for building energy and comfort management based on occupant behaviors. Energy and Buildings 56, 1–7 (2013)
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© 2015 Springer International Publishing Switzerland
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Kadera, P., Macaš, M. (2015). Applying Agents and Genetic Algorithms for Reducing Peak Consumption in District Heating. In: Mařík, V., Schirrmann, A., Trentesaux, D., Vrba, P. (eds) Industrial Applications of Holonic and Multi-Agent Systems. HoloMAS 2015. Lecture Notes in Computer Science(), vol 9266. Springer, Cham. https://doi.org/10.1007/978-3-319-22867-9_18
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DOI: https://doi.org/10.1007/978-3-319-22867-9_18
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