Abstract
These years, the control of energy consumption and the use of renewable energies are becoming more important. In addition, there is a strong transition from a model where production is planned and predictable to another decentralized model that is difficult to predict and that energy management in distributed environments is really difficult, which requires the integration of new techniques such as information and communication technology (ICT), and artificial intelligence. However, with the Internet of Things (IoT), devices can communicate and exchange data with each other. The IoT paradigm promises to increase visibility and awareness of energy consumption through smart sensors and meters in machines and production lines. The connected objects are designed to collect and analyze data in real-time. They thus make it possible to monitor and manage the use and consumption. As a result, real-time energy consumption data from production processes can be easily collected and analyzed to improve the decision making. In this context, this document contributes to an understanding of the energy production management practices made possible by the IoT technic. This technic presents an element of an open system that is always in permanent interaction with other systems. For this, the presence of interoperability is essential in this technic for adapting to other systems already existing or still to be created. In addition, energy managers can adapt the adoption of IoT in a benefit-oriented way, by addressing energy management practices more aligned with the business, data and systems maturity and the information tools available. Finally, this work can be considered as an information platform for designers and installers in the field of intelligent energy management.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Apostolos, F., Alexios, P., Georgios, P., Panagiotis, S., George, C.: Energy efficiency of manufacturing processes: a critical review. In: Procedia CIRP (2013). https://doi.org/10.1016/j.procir.2013.06.044
Driessen, P.H., Hillebrand, B., Kok, R.A.W., Verhallen, T.M.M.: Green new product development: the pivotal role of product greenness. IEEE Trans. Eng. Manag. (2013). https://doi.org/10.1109/TEM.2013.2246792
Madeti, S.R., Singh, S.N.: Monitoring system for photovoltaic plants: a review. Renew. Sustain. Energy Rev. (2017). https://doi.org/10.1016/j.rser.2016.09.088
Vijayaraghavan, A., Dornfeld, D.: Automated energy monitoring of machine tools. CIRP Ann. Manuf. Technol. (2010). https://doi.org/10.1016/j.cirp.2010.03.042
Chloe Depigny, S.F.-P.: How GB could learn from the French smart meter (Linky) programme—Sia Partners UK. 2018 (Online). Available: https://sia-partners.co.uk/gb-learn-french-smart-meter-linky-programme/. Accessed 26 May 2020
Qasem, Y.A.M., Abdullah, R., Yah, Y., Atan, R., Al-Sharafi, M.A., Al-Emran, M.: Towards the development of a comprehensive theoretical model for examining the cloud computing adoption at the organizational level. 63–74 (2021)
Saba, D., Sahli, Y., Abanda, F.H., Maouedj, R., Tidjar, B.: Development of new ontological solution for an energy intelligent management in Adrar city. Sustain. Comput. Informatics Syst. 21, 189–203 (2019). https://doi.org/10.1016/J.SUSCOM.2019.01.009
Pacheco, R., Ordóñez, J., Martínez, G.: Energy efficient design of building: a review. Renew. Sustain. Energy Rev. (2012). https://doi.org/10.1016/j.rser.2012.03.045
Saba, D., Laallam, F.Z., Degha, H.E., Berbaoui, B., Maouedj, R.: Design and development of an intelligent ontology-based solution for energy management in the home. In: Hassanien, A.E. (ed.) Studies in Computational Intelligence, 801st edn, pp. 135–167. Springer, Cham, Switzerland (2019)
Saba, D., Maouedj, R., Berbaoui, B.: Contribution to the development of an energy management solution in a green smart home (EMSGSH). In: Proceedings of the 7th International Conference on Software Engineering and New Technologies—ICSENT, pp. 1–7 (2018). https://doi.org/10.1145/3330089.3330101
Saba, D., Degha, H.E., Berbaoui, B., Maouedj, R.: Development of an Ontology Based Solution for Energy Saving Through a Smart Home in the City of Adrar in Algeria, pp. 531–541. Springer, Cham (2018)
Saba, D., Berbaoui, B., Degha, H.E., Laallam, F.Z.: A generic optimization solution for hybrid energy systems based on agent coordination. (2018)
Saba, D., Degha, H.E., Berbaoui, B., Laallam, F.Z., Maouedj, R.: Contribution to the modeling and simulation of multi- agent systems for energy saving in the habitat. In: International Conference on Mathematics and Information Technology, p. 1 (2017)
Olatomiwa, L., Mekhilef, S., Ismail, M.S., Moghavvemi, M.: Energy management strategies in hybrid renewable energy systems: a review. Renew. Sustain. Energy Rev. (2016). https://doi.org/10.1016/j.rser.2016.05.040
Aldossari, M.Q., Sidorova, A.: Consumer acceptance of internet of things (IoT): smart home context. J. Comput. Inf. Syst. 1–11 (2018). https://doi.org/10.1080/08874417.2018.1543000
Saba, D., Laallam, F.Z., Berbaoui, B., Abanda, F.H.: An energy management approach in hybrid energy system based on agent’s coordination. In: Hassanien, T.M.A, Shaalan, K., Gaber, T., Azar, A. (eds.) Advances in Intelligent Systems and Computing, 533rd, pp. 299–309, Cairo—Egypte: Springer, Cham (2017)
Saba, D., Laallam, F.Z., Belmili, H., Abanda, F.H., Bouraiou, A.: Development of an ontology-based generic optimisation tool for the design of Hybrid energy systems. Int. J. Comput. Appl. Technol. (2017). https://doi.org/10.1504/IJCAT.2017.084773
Saba, D., Laallam, F.Z., Hadidi, A.E., Berbaoui, B.: 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 (2015). https://doi.org/10.1016/j.egypro.2015.07.792
Teo, H.G., Lee, P.S., Hawlader, M.N.A.: An active cooling system for photovoltaic modules. Appl. Energy (2012). https://doi.org/10.1016/j.apenergy.2011.01.017
Saba, D., Laallam, F.Z., Hadidi, A.E., Berbaoui, B.: Optimization of a multi-source system with renewable energy based on ontology. Energy Procedia 74, 608–615 (2015). https://doi.org/10.1016/J.EGYPRO.2015.07.787
Tahersima, F., Madsen, P.P., Andersen, P.: An intuitive definition of demand flexibility in direct load control. In: Proceedings of the IEEE International Conference on Control Applications (2013). https://doi.org/10.1109/cca.2013.6662802
Ruiz, N., Cobelo, I., Oyarzabal, J.: A direct load control model for virtual power plant management. IEEE Trans. Power Syst. (2009). https://doi.org/10.1109/TPWRS.2009.2016607
Arpaci, I., Al-Emran, M., Al-Sharafi, M.A., Shaalan, K.: A novel approach for predicting the adoption of smartwatches using machine learning algorithms. pp. 185–195 (2021)
Kabalci, Y.: A survey on smart metering and smart grid communication. Renew. Sustain. Energy Rev. (2016). https://doi.org/10.1016/j.rser.2015.12.114
Kim, Y., Park, Y., Choi, J.: A study on the adoption of IoT smart home service: using value-based adoption model. Total Qual. Manag. Bus. Excell. 28(9–10), 1149–1165 (2017). https://doi.org/10.1080/14783363.2017.1310708
Haftendorn, C., Holz, F., Hirschhausen, C.V.: The end of cheap coal? A techno-economic analysis until 2030 using the COALMOD-World model. Fuel (2012). https://doi.org/10.1016/j.fuel.2012.04.044
Davis, F.D.: A technology acceptance model for empirically testing new end-user information systems: theory and results. Management (1985). oclc/56932490
Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. Manag. Inf. Syst. (1989). https://doi.org/10.2307/249008
Ajzen, I.: From intentions to actions: a theory of planned behavior. Action Control (1985)
Ajzen, I.: The theory of planned behaviour: reactions and reflections. Psychol. Health. (2011). https://doi.org/10.1080/08870446.2011.613995
Venkatesh, F.D., Morris, V., Davis, M.G., Davis, G.B.: User acceptance of information technology. J. MIS Q. (2003)
Rogers, E.M.: Diffusion of innovations: simon and schuster. Comput. (Long. Beach. Calif) (2010). https://doi.org/10.1109/2.48
Tornatzky, L.G., Fleischer, M.: The Process of Technology Innovation. (1990)
Gücin, N.Ö., Berk, Ö.S.: Technology acceptance in health care: an integrative review of predictive factors and intervention programs. Procedia Soc. Behav. Sci. 195, 1698–1704 (2015). https://doi.org/10.1016/j.sbspro.2015.06.263
King, W.R., He, J.: A meta-analysis of the technology acceptance model. Inf. Manag. 43(6), 740–755 (2006). https://doi.org/10.1016/j.im.2006.05.003
Davis, F.D.: A technology acceptance model for empirically testing new end-user information systems : theory and results. (1985)
Fishbein, M., Ajzen, I.: Attitude, intention and behavior: an introduction to theory and research reading. J. Bus. Ventur. (1975)
Lamberti, F., Manuri, F., Sanna, A., Paravati, G., Pezzolla, P., Montuschi, P.: Challenges, opportunities, and future trends of emerging techniques for augmented reality-based maintenance. IEEE Trans. Emerg. Top. Comput. (2014). https://doi.org/10.1109/TETC.2014.2368833
Williams, M.D., Rana, N.P., Dwivedi, Y.K.: The unified theory of acceptance and use of technology (UTAUT): a literature review. J. Enterp. Inf. Manag. (2015). https://doi.org/10.1108/JEIM-09-2014-0088
Thakur, R., Srivastava, M.: Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Res. (2014). https://doi.org/10.1108/IntR-12-2012-0244
Kalinic, Z., Marinkovic, V.: Determinants of users’ intention to adopt m-commerce: an empirical analysis. Inf. Syst. E-bus. Manag. (2016). https://doi.org/10.1007/s10257-015-0287-2
Rossmann, C.: Theory of reasoned action—theory of planned behavior. (2011)
Lee, Y., Larsen, K.R.: Threat or coping appraisal: determinants of SMB executives’ decision to adopt anti-malware software. Eur. J. Inf. Syst. 18(2), 177–187 (2009). https://doi.org/10.1057/ejis.2009.11
Hsiao, R.L.: Technology fears: distrust and cultural persistence in electronic marketplace adoption. J. Strateg. Inf. Syst. (2003). https://doi.org/10.1016/S0963-8687(03)00034-9
Baird, A., Furukawa, M., Raghu, T.: Understanding contingencies associated with the early adoption of customer-facing web portals. J. Manag. Inf. Syst. (2012). https://doi.org/10.2753/MIS0742-1222290210
Al-Emran, M., Al-Maroof, R., Al-Sharafi, M.A., Arpaci, I.: What impacts learning with wearables? An integrated theoretical model. Interact. Learn. Environ. 1–21 (2020). https://doi.org/10.1080/10494820.2020.1753216
Chaudhuri, A., Cavoukian, A.: The proactive and preventive privacy (3P) framework for IoT privacy by design. EDPACS (2018). https://doi.org/10.1080/07366981.2017.1343548
Gregor, S.: The nature of theory in information systems. MIS Q. Manag. Inf. Syst. (2006). https://doi.org/10.2307/25148742
Weber, R.: Evaluating and developing theories in the information systems discipline. J. Assoc. Inf. Syst. (2012). https://doi.org/10.17705/1jais.00284
Jaccard, J., Jaccoby, J.: Theory construction and model-building skills. In: Science as an Approach to Understanding (2010)
Forbes, I.: The internet of things: from theory to reality how companies are leveraging the IoT to move their businesses forward in association (2017)
Lele, A.: Internet of things (IoT). In: Smart Innovation, Systems and Technologies (2019)
Tan, L., Wang, N.: Future Internet: the internet of things. In: ICACTE 2010—2010 3rd International Conference on Advanced Computer Theory and Engineering, Proceedings (2010). https://doi.org/10.1109/icacte.2010.5579543
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Saba, D., Sahli, Y., Maouedj, R., Hadidi, A. (2021). Energy Management Based on Internet of Things. In: Al-Emran, M., Shaalan, K. (eds) Recent Advances in Technology Acceptance Models and Theories. Studies in Systems, Decision and Control, vol 335. Springer, Cham. https://doi.org/10.1007/978-3-030-64987-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-64987-6_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64986-9
Online ISBN: 978-3-030-64987-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)