Abstract
In smart environments, there is an increasing demand for scalable and autonomous management systems. In this regard, energy efficiency hands out challenging aspects, for both home and business usages. Scalability in energy management systems is particularly difficult in those industry sector where power consumption of branches located in remote areas need to be monitored. Being autonomous requires that behavioural rules are automatically extracted from consumption data and applied to the system. Best practices for the specific energy configuration should be devised to achieve optimal energy efficiency. Best practices should also be revised and applied without human intervention against topology changes. In this paper, the Internet of Things paradigm and machine learning techniques are exploited to (1) define a novel system architecture for centralised energy efficiency in distributed sub-networks of electric appliances, (2) extract behavioural rules, identify best practices and detect device types. A system architecture tailored for autonomous energy efficiency has interesting applications in smart industry—where energy managers may effortlessly monitor and optimally setup a large number of sparse divisions—and smart home—where impaired people may avoid energy waste through an autonomous system that can be employed by the users as a delegate for decision making.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
Cloud integration is deliberately excluded from object abstraction, where the idea of “local” is predominant and high performances are not required.
References
Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: Buneman P, Jajodia S (eds) Proceedings of the 1993 ACM SIGMOD international conference on management of data. ACM Press, Washington, D.C., pp 207–216. https://doi.org/10.1145/170035.170072
Allouhi A, El Fouih Y, Kousksou T, Jamil A, Zeraouli Y, Mourad Y (2015) Energy consumption and efficiency in buildings: current status and future trends. J Clean Prod 109:118–130. https://doi.org/10.1016/j.jclepro.2015.05.139
Anvari-Moghaddam A, Monsef H, Rahimi-Kian A (2015) Optimal smart home energy management considering energy saving and a comfortable lifestyle. IEEE Trans Smart Grid 6(1):324–332. https://doi.org/10.1109/TSG.2014.2349352
Atzori L, Iera A, Morabito G (2010) The Internet of Things: a survey. Comput Netw 54(15):2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010
Baliga J, Ayre R, Hinton K, Sorin WV, Tucker RS (2009a) Energy consumption in optical IP networks. J Lightw Technol 27(13):2391–2403. https://doi.org/10.1109/JLT.2008.2010142
Baliga J, Hinton K, Ayre R, Tucker RS (2009b) Carbon footprint on the internet. telecommunications. J Aust 59(1). https://doi.org/10.2104/tja09005
Bock HH (2007) Clustering methods: a history of k-means algorithms. In: Brito P, Cucumel G, Bertrand P, de Carvalho F (eds) Selected contributions in data analysis and classification. Studies in classification, data analysis, and knowledge organization. Springer, Berlin, pp 161–172. https://doi.org/10.1007/978-3-540-73560-1_15
Bonetto R, Rossi M (2017) Machine learning approaches to energy consumption forecasting in households. CoRR. arXiv:abs/1706.09648
Botta A, de Donato W, Persico V, Pescapè A (2016) Integration of cloud computing and internet of things: a survey. Future Gen Comput Syst 56:684–700. https://doi.org/10.1016/j.future.2015.09.021
Breheny M (1995) The compact city and transport energy consumption. Trans Inst Br Geog 20(1):81–101. https://doi.org/10.2307/622726
Buettner M, Greenstein B, Sample AP, Smith JR, Wetherall D (2008) Revisiting smart dust with RFID sensor networks. In: Williamson CL, Andersen D, Gribble SD (eds) 7th ACM workshop on hot topics in networks—HotNets-VII. Calgary, ACM SIGCOMM, pp 37–42
Burlig F, Knittel C, Rapson D, Reguant M, Wolfram C (2017) Machine learning from schools about energy efficiency. Working Paper 23908, National Bureau of Economic Research. https://doi.org/10.3386/w23908
Byun J, Park S (2011) Development of a self-adapting intelligent system for building energy saving and context-aware smart services. IEEE Trans Consum Electron 57(1):90–98. https://doi.org/10.1109/TCE.2011.5735486
Cristani M, Karafili E, Tomazzoli C (2014a) An ambient intelligence technology for energy saving. In: 27th International conference on industrial, engineering and other applications of applied intelligent Systems (IEA-AIE-2014), Kaohsiung, 3–6 June. Springer
Cristani M, Karafili E, Tomazzoli C (2014b) Energy saving by ambient intelligence techniques. In: Barolli L, Xhafa F, Takizawa M, Enokido T, Castiglione A, Santis AD (eds) 17th International conference on network-based information systems, NBiS 2014, Salerno, IEEE Computer Society, pp 157–164. https://doi.org/10.1109/NBiS.2014.39
Cristani M, Karafili E, Tomazzoli C (2015) Improving energy saving techniques by ambient intelligence scheduling. In: 29th IEEE international conference on advanced information networking and applications, AINA 2015, Gwangju, March 24–27, 2015, pp 324–331. https://doi.org/10.1109/AINA.2015.202
Cristani M, Olivieri F, Tomazzoli C (2016a) Automatic synthesis of best practices for energy consumptions. In: 10th International conference on innovative mobile and internet services in ubiquitous computing, IMIS 2016, Fukuoka, July 6–8, 2016, pp 154–161. https://doi.org/10.1109/IMIS.2016.79
Cristani M, Tomazzoli C, Karafili E, Olivieri F (2016b) Defeasible reasoning about electric consumptions. In: 30th IEEE international conference on advanced information networking and applications, AINA 2016, Crans-Montana, 23–25 March, 2016, pp 885–892. https://doi.org/10.1109/AINA.2016.62
de Camargo RY, Filho FC, Kon F (2009) Efficient maintenance of distributed data in highly dynamic opportunistic grids. In: Shin SY, Ossowski S (eds) Proceedings of the 2009 ACM symposium on applied computing (SAC), Honolulu, ACM, pp 1067–1071. https://doi.org/10.1145/1529282.1529515
Du X, Ang MH, Rus D (2017) Car detection for autonomous vehicle: LIDAR and vision fusion approach through deep learning framework. In: 2017 IEEE/RSJ international conference on intelligent robots and systems, IROS 2017, Vancouver, BC, Canada, September 24–28, 2017, IEEE, pp 749–754. https://doi.org/10.1109/IROS.2017.8202234
Fan X, Weber W, Barroso LA (2007) Power provisioning for a warehouse-sized computer. In: 34th International symposium on computer architecture (ISCA 2007), June 9–13, 2007, San Diego, pp 13–23. https://doi.org/10.1145/1250662.1250665
Figueiredo MB, de Almeida A, Ribeiro B (2012) Home electrical signal disaggregation for non-intrusive load monitoring (NILM) systems. Neurocomputing 96:66–73. https://doi.org/10.1016/j.neucom.2011.10.037
Foster I, Kesselman C (eds) (1999) The grid: blueprint for a new computing infrastructure. Morgan Kaufmann Publishers Inc., San Francisco
García Martín E (2017) Energy efficiency in machine learning : a position paper. In: 30th Annual workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Linköping Electronic Conference Proceedings, vol 137, pp 68–72
Gharaibeh A, Salahuddin MA, Hussini SJ, Khreishah A, Khalil I, Guizani M, Al-Fuqaha AI (2017) Smart cities: a survey on data management, security, and enabling technologies. IEEE Commun Surv Tutor 19(4):2456–2501. https://doi.org/10.1109/COMST.2017.2736886
Giffinger R, Gudrun H (2010) Smart cities ranking: an effective instrument for the positioning of the cities? ACE: Archit City Environ 4(12):7–26. https://doi.org/10.5821/ace.v4i12.2483
Gonizzi P, Ferrari G, Gay V, Leguay J (2015) Data dissemination scheme for distributed storage for IoT observation systems at large scale. Inf Fusion 22:16–25. https://doi.org/10.1016/j.inffus.2013.04.003
Group TC (2008) SMART 2020: enabling the low carbon economy in the information age. Techreport, Global eSustainability Initiative (GeSI). https://gesi.org/public/research/smart-2020-enabling-the-low-carbon-economy-in-the-information-age
Harris ZS (1954) Distributional structure. WORD 10(2–3):146–162. https://doi.org/10.1080/00437956.1954.11659520
Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891. https://doi.org/10.1109/5.192069
Jhunjhunwala A (2017) The people’s grid. IEEE Spectr 54:44–50. https://doi.org/10.1109/MSPEC.2017.7833505
Jhunjhunwala A, Kaur P (2018) Solar energy, DC distribution, and microgrids: ensuring quality power in rural India. IEEE Electr Mag 6:32–39. https://doi.org/10.1109/MELE.2018.2871277
Jinrui N, Fengchun S, Qinglian R (2006) A study of energy management system of electric vehicles. In: 2006 IEEE vehicle power and propulsion conference, pp 1–6. https://doi.org/10.1109/VPPC.2006.364301
Jonassen S (2015) Large-scale real-time data management for engagement and monetization. In: Altingovde IS, Cambazoglu BB, Tonellotto N (eds) Proceedings of the 2015 workshop on large-scale and distributed system for information retrieval, LSDS-IR 2015, Melbourne, ACM, pp 1–2. https://doi.org/10.1145/2809948.2809953
Laughman C, Lee K, Cox R, Shaw S, Leeb S, Norford L, Armstrong P (2003) Power signature analysis. IEEE Power Energy Mag 1(2):56–63. https://doi.org/10.1109/MPAE.2003.1192027
Liang J, Ng SKK, Kendall G, Cheng JWM (2010a) Load signature study part I: basic concept, structure, and methodology. IEEE Trans Power Deliv 25(2):551–560. https://doi.org/10.1109/TPWRD.2009.2033799
Liang J, Ng SKK, Kendall G, Cheng JWM (2010b) Load signature study part II: disaggregation framework, simulation, and applications. IEEE Trans Power Deliv 25(2):561–569. https://doi.org/10.1109/TPWRD.2009.2033800
Maitre J, Glon G, Gaboury S, Bouchard B, Bouzouane A (2015) Efficient appliances recognition in smart homes based on active and reactive power, fast Fourier transform and decision trees. In: Bouchard B, Giroux S, Bouzouane A, Guillet S (eds) Artificial intelligence applied to assistive technologies and smart environments, papers from the 2015 AAAI workshop, Austin, January 25, 2015., AAAI Press, AAAI Workshops, vol WS-16-03
Mansur V, Carreira P, Arsénio AM (2014) A learning approach for energy efficiency optimization by occupancy detection. In: Giaffreda R, Vieriu R, Pásher E, Bendersky G, Jara AJ, Rodrigues JJPC, Dekel E, Mandler B (eds) Internet of Things. User-centric IoT—first international summit, IoT360 2014, Rome, October 27–28, 2014, Revised selected papers, part I, Springer, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 150, pp 9–15. https://doi.org/10.1007/978-3-319-19656-5_2
Martínez-Prieto MA, Cuesta CE, Arias M, Fernández JD (2015) The solid architecture for real-time management of big semantic data. Future Gen Comput Syst 47:62–79. https://doi.org/10.1016/j.future.2014.10.016
Mekuria D, Sernani P, Falcionelli N, Dragoni A (2019) Smart home reasoning systems: a systematic literature review. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-019-01572-z
Mell PM, Grance T (2011) Sp 800-145. the NIST definition of cloud computing. Tech. rep., NIST, Gaithersburg
Merrer EL, Straub G (2011) Distributed overlay maintenance with application to data consistency. In: Hameurlain A, Tjoa AM (eds) Data management in grid and peer-to-peer systems—4th international conference, Globe 2011, Toulouse, Springer, Lecture Notes in Computer Science, vol 6864, pp 25–36. https://doi.org/10.1007/978-3-642-22947-3_3
Nguyen T, Raspitzu A, Aiello M (2014) Ontology-based office activity recognition with applications for energy savings. J Ambient Intell Humaniz Comput 5(5):667–681. https://doi.org/10.1007/s12652-013-0206-7
Nock D, Levin T, Baker E (2020) Changing the policy paradigm: a benefit maximization approach to electricity planning in developing countries. Appl Energy 264:114583. https://doi.org/10.1016/j.apenergy.2020.114583
Pérez-Lombard L, Ortiz J, Pout C (2008) A review on buildings energy consumption information. Energy Build 40(3):394–398. https://doi.org/10.1016/j.enbuild.2007.03.007
Resendes S, Carreira P, Santos A (2014) Conflict detection and resolution in home and building automation systems: a literature review. J Ambient Intell Humaniz Comput 5(5):699–715. https://doi.org/10.1007/s12652-013-0184-9
Ruiz L, Rueda R, Cuéllar M, Pegalajar M (2018) Energy consumption forecasting based on elman neural networks with evolutive optimization. Expert Syst Appl 92:380–389. https://doi.org/10.1016/j.eswa.2017.09.059
Santamouris M, Papanikolaou M, Livada I, Koronakis I, Georgakis C, Argiriou A, Assimakopoulos DN (2001) On the impact of urban climate on the energy consumption of buildings. Solar Energy 70(3):201–216. https://doi.org/10.1016/S0038-092X(00)00095-5
Scannapieco S, Tomazzoli C (2017) Ubiquitous and pervasive computing for real-time energy management and saving a system architecture. Adv Intell Syst Comput 612:3–13. https://doi.org/10.1007/978-3-319-61542-4_1
Seys S, Preneel B (2005) Power consumption evaluation of efficient digital signature schemes for low power devices. In: 2005 IEEE international conference on wireless and mobile computing, networking and communications, WiMob 2005, Montreal, August 22–14, 2005, IEEE, vol 1, pp 79–86. https://doi.org/10.1109/WIMOB.2005.1512820
Siano P, Graditi G, Atrigna M, Piccolo A (2013) Designing and testing decision support and energy management systems for smart homes. J Ambient Intell Humaniz Comput 4(6):651–661. https://doi.org/10.1007/s12652-013-0176-9
Takase W, Matsumoto Y, Hasan A, Lodovico FD, Watase Y, Sasaki T (2014) Experience of a low-maintenance distributed data management system. J Phys Conf Ser 513(3):032095
Tan P, Steinbach M, Kumar V (2005) Introduction to data mining. Addison-Wesley, Boston
The Directorate General for Energy-EU (2019) Clean energy for all Europeans, European Union. https://op.europa.eu/en/publication-detail/-/publication/b4e46873-7528-11e9-9f05-01aa75ed71a1
The European Commission (2019) The European Green Deal, European Union. https://ec.europa.eu/info/sites/info/files/european-green-deal-communication/_en.pdf
Tilak S, Lindquist K, Rajasekar A, Foley S, Hansen T, Orcutt J, Vernon F (2006) ROADNet: a network of SensorNets. In: 38th IEEE conference on local computer networks, pp 579–587. https://doi.org/10.1109/LCN.2006.322019
Tomazzoli C, Cristani M, Karafili E, Olivieri F (2017) Non-monotonic reasoning rules for energy efficiency. J Ambient Intell Smart Environ 9(3):345–360. https://doi.org/10.3233/AIS-170434
Triantafyllopoulos D, Korvesis P, Mporas I, Megalooikonomou V (2016) Real-time management of multimodal streaming data for monitoring of epileptic patients. J Med Syst 40(3):45:1–45:11. https://doi.org/10.1007/s10916-015-0403-3
Tucker RS, Parthiban R, Baliga J, Hinton K, Ayre RWA, Sorin WV (2009) Evolution of WDM optical IP networks: a cost and energy perspective. J Lightw Technol 27(3):243–252. https://doi.org/10.1109/JLT.2008.2005424
Various (2016) International Energy Outlook 2016. Tech. Rep. DOE/EIA-0484 (2016), United States Energy Information Administration
Vastamäki R, Sinkkonen I, Leinonen C (2005) A behavioural model of temperature controller usage and energy saving. Pers Ubiquit Comput 9(4):250–259. https://doi.org/10.1007/s00779-004-0326-3
Weiser M (1991) The computer for the 21st century. Sci Am 265(3):66–75
Whitmore A, Agarwal A, Da Xu L (2015) The Internet of Things—a survey of topics and trends. Inf Syst Front 17(2):261–274. https://doi.org/10.1007/s10796-014-9489-2
Wood G, Newborough M (2003) Dynamic energy-consumption indicators for domestic appliances: environment, behaviour and design. Energy Build 35(8):821–841. https://doi.org/10.1016/S0378-7788(02)00241-4
Xu Z, Jia Q, Guan X, Xie X (2014) A new method to solve large-scale building energy management for energy saving. In: 2014 IEEE international conference on automation science and engineering, CASE 2014, New Taipei, August 18–22, 2014, IEEE, pp 940–945. https://doi.org/10.1109/CoASE.2014.6899439
Yang R, Newman MW, Forlizzi J (2014) Making sustainability sustainable: challenges in the design of eco-interaction technologies. In: Conference on human factors in computing systems—proceedings, pp 823–832
Yu L, Jiang T, Zou Y (2016) Real-time energy management for cloud data centers in smart microgrids. IEEE Access 4:941–950. https://doi.org/10.1109/ACCESS.2016.2539369
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Tomazzoli, C., Scannapieco, S. & Cristani, M. Internet of Things and artificial intelligence enable energy efficiency. J Ambient Intell Human Comput 14, 4933–4954 (2023). https://doi.org/10.1007/s12652-020-02151-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02151-3