Real-Time Home Energy Management with IoT and Blockchain
Balancing Consumption and Peer-to-Peer Trading
Received: 2 March 2024 | Revised: 18 March 2024 | Accepted: 21 March 2024 | Online: 1 June 2024
Corresponding author: Rabie Α. Ramadan
Abstract
As a result of the exponential increase in energy consumption, energy shortages, and augmented energy costs have become a significant problem for households. Solar cells are used in many homes and/or buildings to address these problems. However, managing renewable energy sources in homes can be difficult due to the irregular nature of renewable energy production. Internet of Things (IoT) devices can provide real-time data on energy production and consumption, offering a promising solution to this issue. The current study proposes a framework based on IoT and blockchain technology for home energy management by predicting future energy consumption patterns and optimizing energy use in real time. The blockchain module facilitates peer-to-peer energy trading between renewable energy-generating homeowners and consumers. The proposed framework was tested employing a dataset based on smart homes with solar panels and wind turbines. The results manifest a reduction in energy costs and a possible 30% increase in their traditional gain.
Keywords:
energy consumption, smart homes, peer-to-peer, energy trading, predictionDownloads
References
D. Han and J. Lim, “Smart home energy management system using IEEE 802.15.4 and zigbee,” IEEE Transactions on Consumer Electronics, vol. 56, no. 3, pp. 1403–1410, Aug. 2010.
C. Wang, Y. Zhou, J. Wang, and P. Peng, “A novel Traversal-and-Pruning algorithm for household load scheduling,” Applied Energy, vol. 102, pp. 1430–1438, Feb. 2013.
H. Ghazzai, H. Menouar, A. Kadri, and Y. Massoud, “Future UAV-Based ITS: A Comprehensive Scheduling Framework,” IEEE Access, vol. 7, pp. 75678–75695, 2019.
S. S. Ali, M. Maroof, and S. Hanif, “Smart energy meters for energy conservation & minimizing errors,” in 2010 Joint International Conference on Power Electronics, Drives and Energy Systems & 2010 Power India, New Delhi, India, Dec. 2010, pp. 1–7.
L. Liu, Y. Liu, L. Wang, A. Zomaya, and S. Hu, “Economical and Balanced Energy Usage in the Smart Home Infrastructure: A Tutorial and New Results,” IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 4, pp. 556–570, Oct.. 2015.
A. Rezaee Jordehi, “Enhanced leader particle swarm optimisation (ELPSO): a new algorithm for optimal scheduling of home appliances in demand response programs,” Artificial Intelligence Review, vol. 53, no. 3, pp. 2043–2073, Mar. 2020.
R. Balakrishnan and V. Geetha, “Review on home energy management system,” Materials Today: Proceedings, vol. 47, pp. 144–150, Jan. 2021.
N. Staifi and M. Belguidoum, “Adapted smart home services based on smart contracts and service level agreements,” Concurrency and Computation: Practice and Experience, vol. 33, no. 23, 2021, Art. no. e6208.
J. Han, C. S. Choi, W. K. Park, and I. Lee, “Green Home Energy Management System through comparison of energy usage between the same kinds of home appliances,” in 2011 IEEE 15th International Symposium on Consumer Electronics (ISCE), Singapore, Jun. 2011, pp. 1–4.
A. Amer, K. Shaban, A. Gaouda, and A. Massoud, “Home Energy Management System Embedded with a Multi-Objective Demand Response Optimization Model to Benefit Customers and Operators,” Energies, vol. 14, no. 2, Jan. 2021, Art. no. 257.
S. Davarzani, I. Pisica, G. A. Taylor, and K. J. Munisami, “Residential Demand Response Strategies and Applications in Active Distribution Network Management,” Renewable and Sustainable Energy Reviews, vol. 138, Mar. 2021, Art. no. 110567.
S. Xu et al., “Agent-based modeling and simulation for the electricity market with residential demand response,” CSEE Journal of Power and Energy Systems, vol. 7, no. 2, pp. 368–380, Mar. 2021.
T. Molla, “Smart Home Energy Management System,” in Research Anthology on Smart Grid and Microgrid Development, IGI Global, 2022, pp. 1132–1147.
A. M. S. Saleh, “A Power-Aware Method for IoT Networks with Mobile Stations and Dynamic Power Management Strategy,” Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12108–12114, Dec. 2023.
M. Sojoudi, R. Madatov, T. Sojoudi, and P. Farhadi, “Achieving Steady and Stable Energy from AlGaAsGaAs Solar Cells,” Engineering, Technology & Applied Science Research, vol. 1, no. 6, pp. 151–154, Dec. 2011.
R. A. Ramadan, “Internet of things dataset for home renewable energy management,” Data in Brief, vol. 53, Apr. 2024, Art. no. 110166.
Downloads
How to Cite
License
Copyright (c) 2024 Eissa Jaber Al-Reshidi, Rabie Ramadan, Bassam W. Aboshosha , Marwa Salem , Abdulaziz Mohammed Alayba
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
