Abstract
Energy management systems are a typical example for inert systems where an event or action causes an effect with a delay. Traditional solutions for energy management, such as PID controllers (PID = proportional-integral-derivative loops), control target values efficiently but are sub-optimal in terms of energy consumption. The paper presents a novel, case-based reasoning approach for inert energy management systems that aims to reduce energy wastage in over heating and over cooling for buildings. We develop a case representation based on time series data, taking environmental impact factors into consideration, such as weather forecast data. This includes a post-mortem assessment function that balances energy consumption with comfort for the users. We briefly discuss retrieval and reuse issues. We report on an experimental evaluation of the approach based on a building simulation, including 35 years of historical weather data.
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References
Bergmann, R.: Experience Management: Foundations, Development Methodology, and Internet-Based Applications. Springer, Heidelberg (2002)
Gundersen, O.E.: An analysis of long term dependence and case-based reasoning. In: Workshop Proceedings of ICCBR 2014, pp. 129–138 (2014)
Gundersen, O.E., Bach, K.: RATIC 2016: workshop on reasoning about time in CBR. In: Workshop Proceedings of ICCBR 2016 (2016)
Gundersen, O.E., Montani, S.: RATIC 2014: workshop on reasoning about time in CBR. In: Workshop Proceedings of ICCBR 2014, pp. 125–164 (2014)
Leake, D., Wilson, D.: Taking time to make time: types of temporality and directions for temporal context in CBR. In: Workshop Proceedings of ICCBR 2014, pp. 149–153 (2014)
Ma, Y.: Model predictive control for energy efficient buildings. Ph.D. thesis, UC Berkeley (2012)
Massa, B.: Personal interview on energy cost calculations in facility management, 25-04-2017 at Frankfurt a.M., Germany (2017)
Nise, N.S.: Control Systems Engineering, 6th edn. Wiley, Hoboken (2011)
Platon, R., Martel, J., Zoghlami, K.: CBR model for predicting a building’s electricity use: on-line implementation in the absence of historical data. In: Hüllermeier, E., Minor, M. (eds.) ICCBR 2015. LNCS, vol. 9343, pp. 306–319. Springer, Cham (2015). doi:10.1007/978-3-319-24586-7_21
Richter, M.M., Weber, R.: Case-Based Reasoning: A Textbook. Springer, Heidelberg (2013)
Shabani, A., Paul, A., Platon, R., Hüllermeier, E.: Predicting the electricity consumption of buildings: an improved CBR approach. In: Goel, A., Díaz-Agudo, M.B., Roth-Berghofer, T. (eds.) ICCBR 2016. LNCS (LNAI), vol. 9969, pp. 356–369. Springer, Cham (2016). doi:10.1007/978-3-319-47096-2_24
Acknowledgements
The authors would like to thank Prof. Alfred Karbach and his team at the THM University of Applied Sciences for providing valuable insights to energy systems and many fruitful discussions on user comfort. Further, we thank Dipl.-Ing. Beate Massa whose expert advice on facility management issues we appreciate very much.
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Minor, M., Marx, L. (2017). Case-Based Reasoning for Inert Systems in Building Energy Management. In: Aha, D., Lieber, J. (eds) Case-Based Reasoning Research and Development. ICCBR 2017. Lecture Notes in Computer Science(), vol 10339. Springer, Cham. https://doi.org/10.1007/978-3-319-61030-6_14
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DOI: https://doi.org/10.1007/978-3-319-61030-6_14
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