Abstract
In the last few years, many appliances are spreading into our houses and are daily used. Such equipment significantly improves the quality of life of people, but their use, when not well regulated, can bring a needless increment in the electricity bill. Such an increment could be mitigated by using intelligent scheduling policies that guide the users toward correct exploitation of electric devices so optimizing their use while, at the same time, saving energy, money, and time. This chapter proposes a case study in which a cognitive scheduling approach is used. Such a case study, implemented in the context of the COGITO project, is devoted to automatically scheduling electric loads in houses according to user preferences, self-produced energy, and variable energy costs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
COGITO project—A COGnItive dynamic sysTem to allOw buildings to learn and adapt—https://www.icar.cnr.it/en/progetti/cogito-sistema-dinamico-e-cognitivo-per-consentire-agli-edifici-di-apprendere-ed-adattarsi/.
- 2.
Omnia Energia S.p.A. https://www.omniaenergia.it/.
- 3.
- 4.
ZigBee Home Automation User Guide. https://www.nxp.com/docs/en/user-guide/JN-UG-3076.pdf.
- 5.
Mosquitto MQTT Broker. http://www.steves-internet-guide.com/mosquitto-broker/.
- 6.
- 7.
Grafana Labs. https://grafana.com/.
References
Amjady, N., Hemmati, M.: Energy price forecasting - problems and proposals for such predictions. IEEE Power Energy Mag. 4(2), 20–29 (2006)
Atzori, L., Iera, A., Morabito, G.: The internet of things: A survey. Computer Networks 54(15), 2787–2805 (2010)
Belli, G., Giordano, A., Mastroianni, C., Menniti, D., Pinnarelli, A., Scarcello, L., Sorrentino, N., Stillo, M.: A unified model for the optimal management of electrical and thermal equipment of a prosumer in a dr environment. IEEE Trans. Smart Grid (2017). https://doi.org/10.1109/TSG.2017.2778021
Cicirelli, F., Guerrieri, A., Spezzano, G., Vinci, A., Briante, O., Iera, A., Ruggeri, G.: Edge computing and social internet of things for large-scale smart environments development. IEEE Internet Things J. (99) (2017). https://doi.org/10.1109/JIOT.2017.2775739
Cicirelli, F., Guerrieri, A., Mercuri, A., Spezzano, G., Vinci, A.: Itema: A methodological approach for cognitive edge computing iot ecosystems. Future Gener. Comput. Syst. 92, 189–197 (2019). https://doi.org/10.1016/j.future.2018.10.003. http://www.sciencedirect.com/science/article/pii/S0167739X17330224
Cicirelli, F., Guerrieri, A., Spezzano, G., Vinci, A.: A cognitive enabled, edge-computing architecture for future generation iot environments. In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), pp. 35–40. IEEE (2019)
Cicirelli, F., Gentile, A.F., Greco, E., Guerrieri, A., Spezzano, G., Vinci, A.: An energy management system at the edge based on reinforcement learning. In: 2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), pp. 1–8. IEEE (2020)
Cicirelli, F., Guerrieri, A., Mastroianni, C., Spezzano, G., Vinci, A.: Thermal comfort management leveraging deep reinforcement learning and human-in-the-loop. In: Accepted for the Proc. of the 1st IEEE International Conference on Human-Machine Systems (ICHMS2020) (2020)
Cicirelli, F., Guerrieri, A., Mastroianni, C., Scarcello, L., Spezzano, G., Vinci, A.: Balancing energy consumption and thermal comfort with deep reinforcement learning. In: 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS), pp. 1–6 (2021). https://doi.org/10.1109/ICHMS53169.2021.9582638
Das, S., Cook, D.: Designing and modeling smart environments. In: International Symposium on a World of Wireless, Mobile and Multimedia Networks, 2006. WoWMoM 2006, pp. 5 pp.–494. Buffalo-Niagara Falls, NY (2006). https://doi.org/10.1109/WOWMOM.2006.35
Fioretto, F., Yeoh, W., Pontelli, E.: A multiagent system approach to scheduling devices in smart homes. In: Workshops at the Thirty-First AAAI Conference on Artificial Intelligence (2017)
Li, T., Xiao, Y., Song, L.: Deep reinforcement learning based residential demand side management with edge computing. In: 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 1–6. IEEE (2019)
Lin, Y.-H., Hu, Y-C.: Residential consumer-centric demand-side management based on energy disaggregation-piloting constrained swarm intelligence: Towards edge computing. Sensors 18(5), 1365 (2018)
Mahesh, B.: Machine learning algorithms-a review. Int. J. Sci. Res. (IJSR). [Internet] 9, 381–386 (2020)
Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016)
Noor, A.K.: Potential of cognitive computing and cognitive systems. Open Engineering 5(1) (2015)
Palensky, P., Dietrich, D.: Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE Trans. Ind. Inf. 7(3), 381–388 (2011). https://doi.org/10.1109/TII.2011.2158841
Ploennigs, J., Ba, A., Barry, M.: Materializing the promises of cognitive iot: How cognitive buildings are shaping the way. IEEE Internet Things J. 5(4), 2367–2374 (2017)
Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley (2014)
Shahryari, K., Anvari-Moghaddam, A.: Demand side management using the internet of energy based on fog and cloud computing. In: 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 931–936. IEEE (2017)
Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: Vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press (2018)
Wooldridge, M.: An Introduction to Multiagent Systems. Wiley (2009)
Zhang, D., Han, X., Deng, C.: Review on the research and practice of deep learning and reinforcement learning in smart grids. CSEE J. Power Energy Syst. 4(3), 362–370 (2018)
Acknowledgements
This work has been partially supported by the COGITO (A COGnItive dynamic sysTem to allOw buildings to learn and adapt) project, funded by the Italian government (PON ARS01 00836) and by the CNR project “Industrial transition and resilience of post-Covid19 Societies—Sub-project: Energy Efficient Cognitive Buildings.”
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Cicirelli, F. et al. (2023). Intelligent Load Scheduling in Cognitive Buildings: A Use Case. In: Cicirelli, F., Guerrieri, A., Vinci, A., Spezzano, G. (eds) IoT Edge Solutions for Cognitive Buildings. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-031-15160-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-15160-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15159-0
Online ISBN: 978-3-031-15160-6
eBook Packages: Computer ScienceComputer Science (R0)