IRO Journals
Journal of Information Technology and Digital World

Smart Home Energy Digital Twin with Agentic Optimisation Control

Open Access
Published: 28 August, 2026
Pages: 269-286
  • Kavindu Karunarathne Kavindu Karunarathne
    Department of Computer and Data Science, York St John University, London, United Kingdom
    Department of Computer and Data Science, York St John University, London, United Kingdom
view PDF
view PDF

How to Cite

Karunarathne, Kavindu. 2026. “Smart Home Energy Digital Twin With Agentic Optimisation Control”. Journal of Information Technology and Digital World 8 (3): 269-86. https://doi.org/10.36548/jitdw.2026.3.010.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

Digital Twin
Smart Home Energy Management
Mixed-Integer Linear Programming
Battery Scheduling
Time-of-use Tariff
Agentic Control
Solar Photovoltaics

Abstract

Dynamic time-of-use electricity tariffs expose households to half-hourly price volatility, yet most domestic loads and battery systems are still operated on static schedules that ignore this signal. This research presents a smart home energy digital twin that couples real half-hourly tariff data from the Octopus Energy Agile API with historical solar radiation data from OpenMeteo, a physics-informed home battery model incorporating charge and discharge efficiency and an explicit cycling degradation cost, and a deferrable appliance load. Three autonomous control strategies are evaluated within the digital twin: a static baseline controller; a forecastaware agent that applies interpretable price look-ahead rules for battery arbitrage and appliance scheduling; and a mixed-integer linear programming (MILP) optimiser that computes a costminimal 24-hour schedule subject to battery dynamics, energy-balance, and appliance-deadline constraints. A 30-day backtest over December 2024, selected as a deliberately adverse low-solar and high-volatility stress period, shows that the forecast-aware agent reduces the effective energy cost by 21.41% (£28.20) relative to the baseline, while the MILP optimiser achieves a 27.71% reduction (£36.50) and is cheaper on every one of the 30 days. An analytical derivation and a sensitivity analysis confirm that the agent’s decision thresholds are economically grounded and that its performance is robust across wide threshold ranges. Notably, the optimiser attains the lowest cost while importing slightly more grid energy, demonstrating that intelligent load shifting into low-price windows, rather than import minimisation, drives cost reduction under dynamic pricing.

References

  1. Grieves, Michael. "Digital Twin: Manufacturing Excellence Through Virtual Factory Replication." White Paper 1, 2014, no. 2014: 1-7.
  2. Tao, Fei, He Zhang, Ang Liu, and Andrew YC Nee. "Digital Twin in Industry: State-of-the-Art." IEEE Transactions on industrial informatics 2018, vol. 15, no. 4: 2405-2415.
  3. Semeraro, Concetta, Mario Lezoche, Hervé Panetto, and Michele Dassisti. "Digital Twin Paradigm: A Systematic Literature Review." Computers in Industry 2021, vol. 130: 103469.
  4. Beaudin, Marc, and Hamidreza Zareipour. "Home Energy Management Systems: A Review of Modelling and Complexity." Energy Solutions to Combat Global Warming (2016): 753-793.
  5. Zhou, Bin, Wentao Li, Ka Wing Chan, Yijia Cao, Yonghong Kuang, Xi Liu, and Xiong Wang. "Smart home Energy Management Systems: Concept, Configurations, and Scheduling Strategies." Renewable and Sustainable Energy Reviews 2016, vol. 6, 30-40.
  6. Mahapatra, Bandana, and Anand Nayyar. "Home Energy Management System (HEMS): Concept, Architecture, Infrastructure, Challenges and Energy Management Schemes: B. Mahapatra, A. Nayyar." Energy Systems 2022, vol. 13, no. 3: 643-669.
  7. Mohsenian-Rad, Amir-Hamed, and Alberto Leon-Garcia. "Optimal Residential Load Control with Price Prediction in Real-Time Electricity Pricing Environments." IEEE Transactions on Smart Grid 1, 2010, no. 2: 120-133.
  8. Chen, Zhi, Lei Wu, and Yong Fu. "Real-Time Price-based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization." IEEE transactions on smart grid 3, 2012, no. 4: 1822-1831.
  9. Sou, Kin Cheong, James Weimer, Henrik Sandberg, and Karl Henrik Johansson. "Scheduling Smart Home Appliances using Mixed Integer Linear Programming." In 50th IEEE conference on decision and control and European control conference, IEEE, 2011, 5144-5149.
  10. O'Neill, Daniel, Marco Levorato, Andrea Goldsmith, and Urbashi Mitra. "Residential demand response using reinforcement learning." In First IEEE international conference on smart grid communications, IEEE, 2010, 409-414.
  11. Vázquez-Canteli, José R., and Zoltán Nagy. "Reinforcement Learning for Demand Response: A Review of Algorithms and Modeling Techniques." Applied energy 2019, vol. 235, 1072-1089.
  12. Yu, Liang, Weiwei Xie, Di Xie, Yulong Zou, Dengyin Zhang, Zhixin Sun, Linghua Zhang, Yue Zhang, and Tao Jiang. "Deep Reinforcement Learning for Smart Home Energy Management." IEEE Internet of Things Journal 2019, vol. 7, no. 4: 2751-2762.
  13. Agostinelli, Sofia, Fabrizio Cumo, Giambattista Guidi, and Claudio Tomazzoli. "Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence." Energies 2021, vol. 14, no. 8: 2338.
  14. Onile, Abiodun E., Ram Machlev, Eduard Petlenkov, Yoash Levron, and Juri Belikov. "Uses of the Digital Twins Concept for Energy Services, Intelligent Recommendation Systems, and Demand Side Management: A Review." Energy Reports 2021, vol. 7: 997-1015.
  15. O’Dwyer, Edward, Indranil Pan, Salvador Acha, and Nilay Shah. "Smart Energy Systems for Sustainable Smart Cities: Current Developments, Trends and Future Directions." Applied energy 2019, vol. 237: 581-597.
  16. Mitchell, Stuart, Michael OSullivan, and Iain Dunning. "Pulp: a Linear Programming Toolkit for Python." The University of Auckland, Auckland, New Zealand, 2011, vol. 65, no. 25: 1-12.
  17. Octopus Energy, “Agile Octopus Product and Public REST API Documentation,” [Online]. Available: https://developer.octopus.energy/ (accessed January 2026).
  18. Open-Meteo, “Historical Weather API,” [Online]. Available: https://open-meteo.com/ (accessed January 2026).