95
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A secure energy trading in a smart community by integrating Blockchain and machine learning approach

ORCID Icon, ORCID Icon &
Pages 105-120 | Received 31 Jan 2023, Accepted 10 Oct 2023, Published online: 23 Oct 2023

References

  • Affia I, Aamer A. An internet of things-based smart warehouse infrastructure: design and application. J Sci Technol Policy Manage. 2021;13(1):90–109. doi: 10.1108/JSTPM-08-2020-0117
  • Babu T, Swathi P. Internet of Things (iot) & big data analytics for smart cities-A case study. SSRN Electron J. 2018. doi: 10.2139/ssrn.3167771
  • Bibri S, Krogstie J. A scholarly back casting approach to a novel model for smart sustainable cities of the future: strategic problem orientation. City Territ Archit. 2019;6(1). doi: 10.1186/s40410-019-0102-3
  • Abd-EL-Latif A, Abd-El-Atty B, Venegas-Andraca S. A novel image steganography technique based on quantum substitution boxes. Opt Laser Technol. 2019;116:92–102. doi: 10.1016/j.optlastec.2019.03.005
  • Liang Y, Niu D, Hong W-C. Short-term load forecasting based on feature extraction and improved general regression neural network model. Energy. 2019;166:653–663. doi: 10.1016/j.energy.2018.10.119
  • Sankar VCJ, Lokesh KJ, Nair MG. Multi-agent-based load management system for smart distribution grid. Third International Conference on Smart Systems and Inventive Technology (ICSSIT); 2020 Aug 20-22; Tirunelveli, India.
  • Sreeram Preetha PK, Poornachandran P. Electric vehicle scenario in India: roadmap, challenges, and opportunities. IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT); 2019. Feb 20-22; Coimbatore, India.
  • Tata N, Machiraju SS, Akshay V, et al. Prediction of energy consumption using statistical and machine learning methods and analyzing the significance of climate and holidays in the demand prediction. 2021:736.
  • Gao T, Gong X, Zhang K, et al. A recalling-enhanced recurrent neural network: conjugate gradient learning algorithm and its convergence analysis. Inf Sci. 2020;519:273–288. doi: 10.1016/j.ins.2020.01.045
  • Wang J, Li P, Ran R, et al. A short-term photovoltaic power prediction model based on the Gradient Boost Decision Tree. Appl Sci. 2018;8(5):689. doi: 10.3390/app8050689
  • Seema PN, Gopalan GK, Nair MG. Load forecasting by the main grid controller using ANN and the implementation of demand response using micro-controller. Fourth International Conference on Electrical, Computer and Communication Technologies; 2021 doi: Sep 15-17; Erode, India.
  • Wu Y, Wu Y, Cimen H, et al. P2P energy trading: blockchain-enabled P2P energy society with multi-scale flexibility services. Energy Rep. 2022;8:3614–3628. doi: 10.1016/j.egyr.2022.02.074
  • Salmani H, Rezazade A, Sedighizadeh M. Stochastic peer-to-peer energy trading among charging station of electric vehicles based on blockchain mechanism. IET Smart Cities. 2022;4(2):110–126. doi: 10.1049/smc2.12029
  • Yahaya A, Javaid N, Javed M, et al. Blockchain based secure energy trading with mutual verifiable fairness in a smart community. IEEE Trans Ind Inf. 2022;18(11):7412–7422. doi: 10.1109/TII.2022.3141867
  • Huang X, Zhang Y, Li D, et al. A solution for bi-layer energy trading management in microgrids using multi-blockchain. IEEE Int Things J. 2022;9(15):13886–13900. doi: 10.1109/jiot.2022.3142815
  • Madathil D, Anjali A, Gayathri N, et al. An energy management control strategy for efficient scheduling of domestic appliances in residential buildings. In: Innovations in Power and Advanced Computing Technologies (i-PACT). India: Vellore; 2019 Feb 22-23;1–6.
  • Ajayi RO, Alaka H, Sulaimon I, et al. Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. J Buil Eng. 2022;45:103406. doi: 10.1016/j.jobe.2021.103406
  • Eseye AT, Lehtonen M, Tukia T, et al. Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems. IEEE Access. 2019;7:91463–91475. doi: 10.1109/ACCESS.2019.2924685
  • Shahbazi Z, Byun Y-C. Integration of blockchain, IoT, and machine learning for multistage quality control and enhancing security in smart manufacturing. Sensors. 2021;21(4):1467. doi: 10.3390/s21041467
  • Mrabet H, Alhomoud A, Jemai A, et al. A Secured industrial internet-of-Things architecture based on blockchain technology and machine learning for Sensor Access control systems in smart manufacturing. Appl Sci. 2022;12(9):4641. doi: 10.3390/app12094641
  • Shrivastava A, Krishna KM, Rinawa ML, et al. Inclusion of IoT, ML, and blockchain technologies in next generation Industry 4.0 environment. Mater Today Proc. 2023;80:3471–3475. doi: 10.1016/j.matpr.2021.07.273
  • Tanwar S, Bhatia Q, Patel P, et al. Machine learning adoption in blockchain-based smart applications: the challenges, and a way forward. IEEE Access. 2019;8:474–488. doi: 10.1109/ACCESS.2019.2961372
  • Sugitha G, Solairaj A, Suresh J. Blockchain fostered cycle‐consistent generative adversarial network framework espoused intrusion detection for protecting IoT network. Trans Emerging Tel Tech. 2022;33(11). doi: 10.1002/ett.4578
  • Gregoratti D, Matamoros J. Distributed energy trading: the multiple-microgrid case. IEEE Trans Ind Electron. 2015;62(4):2551–2559. doi: 10.1109/TIE.2014.2352592
  • Zhang C, Wu J, Zhou Y, et al. Peer-to-peer energy trading in a microgrid. Appl Energy. 2018;220:1–12. doi: 10.1016/j.apenergy.2018.03.010
  • Saxena N, Grijalva S, Chukwuka V, et al. Network security and privacy challenges in smart vehicle-to-grid. IEEE Wireless Commun. 2017;24(4):88–98. doi: 10.1109/MWC.2016.1600039WC
  • AJ S, P PK, Nair MG. Smart contract-based energy trading -an overview. 2022 IEEE 19th India Council International Conference (INDICON); 2023 Nov 24-26; Kochi, India.
  • Angrish A, Craver B, Hasan M, et al. A case study for blockchain in manufacturing: “FabRec”: a prototype for peer-to-peer network of manufacturing nodes. Procedia Manuf. 2018;26:26; 1180–1192. doi: 10.1016/j.promfg.2018.07.154
  • Hou W, Guo L, Ning Z. Local electricity storage for blockchain-based energy trading in industrial internet of Things. IEEE Trans Ind Inform. 2019;15(6):3610–3619. doi: 10.1109/TII.2019.2900401
  • Jadhav AM, Patne NR. Priority-based energy scheduling in a smart distributed network with multiple microgrids. IEEE Trans Ind Inform. 2017;13(6):3134–3143. doi: 10.1109/TII.2017.2671923
  • Funde NA, Dhabu MM, Deshpande PS, et al. SF-OEAP: starvation-free optimal energy allocation policy in a smart distributed multi-microgrid system. IEEE Trans Ind Inform. 2018;14(11):4873–4883. doi: 10.1109/TII.2018.2810816
  • Christidis K, Devetsikiotis M. Blockchains and smart contracts for the internet of things. IEEE Access. 2016;4:2292–2303. doi: 10.1109/ACCESS.2016.2566339
  • Park S, Lee J, Bae S, et al. Contribution-based energy-trading mechanism in microgrids for future smart grid: a game theoretic approach. IEEE Trans Ind Electron. 2016;63(7):4255–4265. doi: 10.1109/TIE.2016.2532842
  • Hu J, An E, Sun Q, et al. Distributed multi-energy trading for energy internet: an aggregative game approach. CSEE J Power Energy Syst. 2022;1–9. doi: 10.17775/CSEEJPES.2021.01990
  • Zhang Y, Li J, Zheng D, et al. Privacy-preserving communication and power injection over vehicle networks and 5G smart grid slice. J Network Comput Appl. 2018;122:50–60. doi: 10.1016/j.jnca.2018.07.017
  • Javed MU, Javaid N, Aldegheishem A, et al. Scheduling charging of electric vehicles in a secured manner by emphasizing cost minimization using blockchain technology and IPFS. Sustainability. 2020;12(12):5151. doi: 10.3390/su12125151
  • Samuel O, Javaid N. A secure blockchain–based demurrage mechanism for energy trading in smart communities. Int J Energy Res. 2021;45(1):297–315. doi: 10.1002/er.5424
  • Yahaya AS, Javaid N, Alzahrani FA, et al. Blockchain-based sustainable local energy trading considering home energy management and demurrage mechanism. Sustainability. 2020;12(8):3385. doi: 10.3390/su12083385
  • Kang J, Yu R, Huang X, et al. Enabling localized peer-to-peer electricity trading among plug-in hybrid electric vehicles using consortium blockchains. IEEE Trans Ind Inform. 2017;13(6):3154–3164. doi: 10.1109/TII.2017.2709784
  • Javed MU, Rehman M, Javaid N, et al. Blockchain-based secure data storage for distributed vehicular networks. Appl Sci. 2020;10(6):2011. doi: 10.3390/app10062011
  • Khalid R, Javaid N, Almogren A, et al. A blockchain-based load balancing in decentralized hybrid P2P energy trading market in smart grid. IEEE Access. 2020;8:47047–47062. doi: 10.1109/ACCESS.2020.2979051
  • Yahaya AS, Javaid N, Javed MU, et al. Blockchain-based energy trading and load balancing using contract theory and reputation in a smart community. IEEE Access. 2020;8:222168–222186. doi: 10.1109/ACCESS.2020.3041931
  • Wang W, Hoang DT, Hu P, et al. A survey on consensus mechanisms and mining strategy management in blockchain networks. IEEE Access. 2019;7:22328–22370. doi: 10.1109/ACCESS.2019.2896108
  • Samuel O, Almogren A, Javaid A, et al. Leveraging blockchain technology for secure energy trading and least-cost evaluation of decentralized contributions to electrification in sub-saharan Africa. Entropy. 2020;22(2):226. doi: 10.3390/e22020226
  • Knirsch F, Unterweger A, Engel D. Privacy-preserving blockchain-based electric vehicle charging with dynamic tariff decisions. Comput Sci Res Dev. 2018;33(1–2):71–79. doi: 10.1007/s00450-017-0348-5
  • Su Z, Wang Y, Xu Q, et al. A secure charging scheme for electric vehicles with smart communities in energy blockchain. IEEE Int Things J. 2018;6(3):4601–4613. doi: 10.1109/JIOT.2018.2869297
  • Li Z, Kang J, Yu R, et al. Consortium blockchain for secure energy trading in industrial internet of things. IEEE Trans Ind Inform. 2017;14:3690–3700. doi: 10.1109/TII.2017.2786307

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.