Optimization of Off-Grid Solar Lighting Systems Using OEMOF Integrated with IoT Field Data — Case Study: Bukit Kunci, Indonesia

Authors

  • Eva Hertnacahyani Herraprastanti Diponegoro University Indonesia
  • Berkah Fajar Tamtomo Kiono Diponegoro University Indonesia
  • Ismoyo Haryanto Diponegoro University Indonesia
  • Muchammad Muchammad Diponegoro University Indonesia
  • Muhammad Safar Korai Mehran UET Jamshoro Pakistan

DOI:

https://doi.org/10.26877/asset.v8i1.2672

Keywords:

OEMOF, off-grid systems, energy optimization, rural tourism energy, LCOE, IoT monitoring

Abstract

The growing need for efficient night lighting in natural tourist destinations highlights the importance of reliable and sustainable energy solutions. This study analyzes the optimization of solar-based lighting at Bukit Kunci, Indonesia, using the Open Energy Modelling Framework (OEMOF) combined with real-time monitoring via the IoT ThingSpeak platform. Photovoltaic (PV) panel data recorded at 15-second intervals during February–July 2025, yielding 532,520 records, were cleaned and aggregated as input to model the interaction of PV, batteries, LED lights, inverters, and backup generators, to minimize lifecycle cost and energy loss. Results indicate that the current PV capacity (0.4 kWp) supplies less than 50% of lighting demand, with a high levelized cost of energy (≈9.2 USD/kWh) and low reliability (self-sufficiency 3–22%). Optimization through capacity expansion (≈224 modules, ≈1.25 kWh storage) eliminated load loss probability and reduced LCOE to ≈0.05 USD/kWh. This approach demonstrates OEMOF’s potential to enhance system efficiency, ensure reliable night lighting, and support eco-tourism while offering replicability for rural destinations.

Author Biographies

  • Eva Hertnacahyani Herraprastanti, Diponegoro University

    Faculty of Technology, Universitas Diponegoro, Jl. Prof. Jacub Rais, Tembalang, Kec. Tembalang, Kota Semarang, Jawa Tengah 50275, Central Java, Indonesia

  • Berkah Fajar Tamtomo Kiono, Diponegoro University

    Faculty of Technology, Universitas Diponegoro, Jl. Prof. Jacub Rais, Tembalang, Kec. Tembalang, Kota Semarang, Jawa Tengah 50275, Central Java, Indonesia

  • Ismoyo Haryanto, Diponegoro University

    Faculty of Technology, Universitas Diponegoro, Jl. Prof. Jacub Rais, Tembalang, Kec. Tembalang, Kota Semarang, Jawa Tengah 50275, Central Java, Indonesia

  • Muchammad Muchammad, Diponegoro University

    Faculty of Technology, Universitas Diponegoro, Jl. Prof. Jacub Rais, Tembalang, Kec. Tembalang, Kota Semarang, Jawa Tengah 50275, Central Java, Indonesia

  • Muhammad Safar Korai, Mehran UET Jamshoro

    Institute of Environmental Engineering and Management, Mehran UET Jamshoro, Sindh, Pakistan

References

[1] Hilpert S, Kaldemeyer C, Krien U, et al. The Open Energy Modelling Framework (oemof) - A novel approach in energy system modelling. Energy Strateg Rev 2018;22:16–25. https://doi.org/10.1016/j.esr.2018.07.001.

[2] Candas S, Muschner C, Buchholz S, et al. Code exposed: Review of five open-source frameworks for modeling renewable energy systems. Renew Sustain Energy Rev 2022;161:112272. https://doi.org/10.1016/j.rser.2022.112272.

[3] Hilpert S, Günther S, Söthe M. oemof.tabular – Introducing Data Packages for Reproducible Workflows in Energy System Modeling. J Open Res Softw 2021;9:1–9. https://doi.org/10.5334/JORS.320.

[4] Hilpert S, Kaldemeyer C, Krien U, et al. The Open Energy Modelling Framework (oemof) - A new approach to facilitate open science in energy system modelling. Energy Strateg Rev 2018;22:16–25. https://doi.org/10.1016/j.esr.2018.07.001.

[5] Maruf MNI. A novel method for analyzing highly renewable and sector-coupled subnational energy systems-case study of schleswig-holstein. Sustain 2021;13. https://doi.org/10.3390/su13073852.

[6] Hillen M, Schönfeldt P, Groesdonk P, et al. Integration of a Europe-wide public building database with retrofit strategies and a thermal inertia model into an open-source optimization framework. IOP Conf Ser Earth Environ Sci 2024;1363. https://doi.org/10.1088/1755-1315/1363/1/012013.

[7] Fleischmann J, Delgado Arroyo L, Dunks C, et al. Site-tailored configuration of integrated water-energy-food-environment systems using open software - case study of two Colombian sites. Energy Nexus 2025;19:100507. https://doi.org/https://doi.org/10.1016/j.nexus.2025.100507.

[8] Limpens G, Moret S, Jeanmart H, et al. EnergyScope TD: A novel open-source model for regional energy systems. Appl Energy 2019;255:113729. https://doi.org/10.1016/j.apenergy.2019.113729.

[9] Muthukumaran G, Passos MV, Gong J, et al. Decentralized solutions for island states: Enhancing energy resilience through renewable technologies. Energy Strateg Rev 2024;54:101439. https://doi.org/10.1016/j.esr.2024.101439

[10] Niringiyimana E, Wanquan S, Dushimimana G, et al. Hybrid Renewable Energy System Design and Optimization for Developing Countries Using HOMER Pro: Case of Rwanda. 2023 7th Int. Conf. Green Energy Appl. ICGEA 2023, Zhejiang: IEEE Xplore; 2023, p. 72–6. https://doi.org/10.1109/ICGEA57077.2023.10125739.

[11] Bahramara S, Moghaddam MP, Haghifam MR. Optimal planning of hybrid renewable energy systems using HOMER: A review. Renew Sustain Energy Rev 2016;62:609–20. https://doi.org/10.1016/j.rser.2016.05.039.

[12] Cheikh I, Aouami R, Sabir E, et al. Multi-Layered Energy Efficiency in LoRa-WAN Networks: A Tutorial. IEEE Access 2022;10:9198–231. https://doi.org/10.1109/ACCESS.2021.3140107.

[13] Rhesri A, Bennani R, Maissa Y Ben, et al. Development of a low-cost Internet of Things architecture for energy and environment monitoring in a University Campus. Proc - 2023 Int Conf Futur Internet Things Cloud, FiCloud 2023 2023:181–5. https://doi.org/10.1109/FiCloud58648.2023.00034.

[14] Xiong L. Harnessing personal data from Internet of Things: Privacy enhancing dynamic information monitoring. 2015 Int. Conf. Collab. Technol. Syst., IEEE; 2015, p. 37–37. https://doi.org/10.1109/CTS.2015.7210393.

[15] Fotiou N, Halkiopoulos C, Antonopoulou H. Enhancing Tourism Sustainability Through Blockchain, AI, and Smart Technologies. A Comprehensive Analysis, 2025, p. 193–227. https://doi.org/10.1007/978-3-031-78471-2_8.

[16] Valett L, Bollenbach J, Keller R. Empowering sustainable hotels: a guest-centric optimization for vehicle-to-building integration. Energy Informatics 2024;7. https://doi.org/10.1186/s42162-024-00400-9.

[17] Hajinejad A, Seraj F, Jahangir MH, et al. Economic optimization of hybrid renewable energy systems supplying electrical and thermal loads of a tourist building in different climates. Front Built Environ 2023;8:1–17. https://doi.org/10.3389/fbuil.2022.969293.

[18] Mustafa ATM, Chowdhury M, Sultana J, et al. XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE IoT-based Efficient Streetlight Controlling, Monitoring and Real-time Error Detection System in Major Bangladeshi Cities n.d.

[19] Nikpour M, Behvand P, Jafarzadeh H, et al. Intelligent Energy Management in Smart Cities : Leveraging IoT and Machine Learning for Optimizing Complex Networks and Systems 1- Introduction. ArXiv Prepr ArXiv230605567 2023:1–49.

[20] Witczak D, Szymoniak S. Review of Monitoring and Control Systems Based on Internet of Things. Appl Sci 2024;14. https://doi.org/10.3390/app14198943.

[21] Bangowan P. Desa Wisata Bangowan Kecamatan Jiken Kabupaten Blora Provinsi Jawa Tengah. 2020.

[22] Guo H, Zhou Z, Zhao D, et al. EGNN : Energy-E cient Anomaly Detection for IoT Multivariate Time Series Data Using Graph Neural Network 2023:1–15.

[23] Guo H, Zhou Z, Zhao D. GNN-Based Energy-Efficient Anomaly Detection for IoT Multivariate Time-Series Data. ICC 2023 - IEEE Int Conf Commun 2023:2492–7. https://doi.org/10.1109/ICC45041.2023.10278988.

[24] Ni S, Brockmann G, Darbandi A, et al. Modelling and optimization of a decarbonized heat supply in suburban areas using the Open Energy Modelling Framework. 37th Int Conf Effic Cost, Optim Simul Environ Impact Energy Syst ECOS 2024 2024;3:2230–9. https://doi.org/10.52202/077185-0191.

[25] Madani SS, Shabeer Y, Allard F, et al. A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis. Batteries 2025;11:1–68. https://doi.org/10.3390/batteries11040127

[26] Sutikno T, Purnama HS, Pamungkas A, et al. Internet of things-based photovoltaics parameter monitoring system using NodeMCU ESP8266 2021;11:5578–87. https://doi.org/10.11591/ijece.v11i6.pp5578-5587

[27] Bovera F, Schiavo L Lo, Vailati R. Combining Forward-Looking Expenditure Targets and Fixed OPEX-CAPEX Shares for a Future-Proof Infrastructure Regulation: the ROSS Approach in Italy. Curr Sustain Energy Reports 2024;11:105–15. https://doi.org/10.1007/s40518-024-00239-4

[28] Almas, Sundaram S. Impact of Field Degradation Rates on the Levelized Cost of Energy (LCOE) for a roof-top Solar PV System. 2025 IEEE 53rd Photovolt. Spec. Conf., IEEE; 2025, p. 0564–7. https://doi.org/10.1109/PVSC59419.2025.11133123.

[29] Emblemsvåg J. Rethinking the “Levelized Cost of Energy”: A critical review and evaluation of the concept. Energy Res Soc Sci 2025;119:103897. https://doi.org/10.1016/j.erss.2024.103897.

[30] Emblemsvåg J. Energy Research & Social Science Rethinking the “ Levelized Cost of Energy ” : A critical review and evaluation of the concept. Energy Res Soc Sci 2025;119:103897. https://doi.org/10.1016/j.erss.2024.103897.

Downloads

Published

2026-01-31