Integrasi PLTB berbasis IoT untuk Mendukung SDG 7: Energi Bersih menuju Kota Cerdas Netral Karbon
Keywords:
Internet of Things, Pembangkit Listrik Tenaga Bayu, Smart GridAbstract
Electrical energy is an essential requirement in modern life, as nearly all human activities depend on its availability. In Indonesia, electricity demand continues to increase, while energy distribution remains uneven and is still largely dependent on fossil fuels, resulting in high carbon emissions. This condition presents a significant challenge to achieving Sustainable Development Goal (SDG) 7, which promotes access to affordable and clean energy. One strategic solution is the utilization of wind energy through Wind Power Plants (PLTB), considering Indonesia’s substantial wind energy potential, particularly in coastal and highland regions. This paper proposes the integration of Internet of Things (IoT)-based PLTB as a conceptual innovation to support SDG 7 and accelerate the development of carbon-neutral smart cities. The proposed concept integrates real-time turbine performance monitoring, electricity distribution management through smart grids, and artificial intelligence-based predictive maintenance to improve operational efficiency and system reliability. The development process adopts the Four-D (4D) Research and Development (R&D) model, consisting of Define, Design, Develop, and Disseminate, beginning with energy needs analysis and culminating in the dissemination of the proposed concept to relevant stakeholders. The proposed framework is expected to provide efficient and accessible renewable energy, support the transition toward carbon-neutral smart cities, and strengthen the sustainability of Indonesia’s national energy system.
ABSTRAK
Energi listrik merupakan kebutuhan esensial dalam kehidupan modern karena hampir seluruh aktivitas manusia bergantung pada ketersediaannya. Di Indonesia, konsumsi energi terus meningkat, sementara distribusinya masih belum merata dan sebagian besar masih bergantung pada energi fosil yang menghasilkan emisi karbon tinggi. Kondisi tersebut menjadi tantangan dalam mewujudkan Sustainable Development Goal (SDG) 7 yang berfokus pada penyediaan energi bersih dan terjangkau. Salah satu solusi strategis adalah pemanfaatan energi angin melalui Pembangkit Listrik Tenaga Bayu (PLTB), mengingat Indonesia memiliki potensi energi angin yang besar, khususnya di wilayah pesisir dan dataran tinggi. Artikel ini mengusulkan integrasi PLTB berbasis Internet of Things (IoT) sebagai sebuah gagasan inovatif untuk mendukung pencapaian SDG 7 sekaligus mendorong transformasi menuju kota cerdas netral karbon. Konsep yang diusulkan mengintegrasikan pemantauan kinerja turbin secara real-time, pengelolaan distribusi listrik melalui smart grid, serta pemeliharaan prediktif berbasis kecerdasan buatan guna meningkatkan efisiensi operasional dan keandalan sistem. Pengembangan gagasan dilakukan menggunakan model penelitian dan pengembangan Research and Development (R&D) Four-D (4D), yang meliputi Define, Design, Develop, dan Disseminate, dimulai dari analisis kebutuhan energi hingga penyebarluasan konsep kepada para pemangku kepentingan. Gagasan ini diharapkan mampu menghadirkan energi terbarukan yang efisien dan mudah diakses, mendukung transformasi menuju kota cerdas netral karbon, serta memperkuat keberlanjutan sistem energi nasional.
References
Alnavis, N. B., Wirawan, R. R., Solihah, K. I., & Nugroho, V. H. (2024). Energi Listrik Berkelanjutan: Potensi dan Tantangan Penyediaan Energi Listrik di Indonesia. Journal of Innovation Materials, Energy, and Sustainable Engineering, 1(2), 119–139. https://doi.org/10.61511/jimese.v1i2.2024.544
Al Muhajir, F., & Sinaga, N. (2021). Tinjauan Pemanfaatan Energi Bayu sebagai Pembangkit Listrik di Provinsi Sulawesi Selatan. Teknika, 15(1), 55–61. https://doi.org/10.5281/zenodo.7326888
Anindhita, Santosa, J., & Tokimatsu, K. (2025). Carbon Capture in Indonesia’s Energy Sector: A Least-cost Optimization Approach. Sustainability, 17, 7916. https://doi.org/10.3390/su17177916
Dhake, R., Warade, S., Zanje, S., Shaikh, R., Bhandare, A., & Yeole, R. (2026). Advance IoT and Machine Learning Framework for Predictive Maintenance of Small-Scale Wind Turbines. In J. C. Bansal, P. Jamwal, & S. Hussain (Eds.), Sustainable computing and intelligent systems (Lecture Notes in Networks and Systems, Vol. 1928). Springer. https://doi.org/10.1007/978-3-032-22917-5_18
Electric and Power Indonesia. (17 April 2026). Potensi PLTB Indonesia: Menggali 246 GW Energi Angin dari NTT hingga Papua. https://www.electricindonesia.com/potensi-pltb-indonesia-menggali-246-gw-energi-angin-dari-ntt-hingga-p apua/
Fajri, S., & Yuhendri, M. (2023). Monitoring Pembangkit Listrik Tenaga Angin Menggunakan Human Machine Interface. JTEIN: Jurnal Teknik Elektro Indonesia, 4(1), 434–444. https://doi.org/10.24036/jtein.v4i1.421
Fauzi, P. R. (2023). Peluang dan Tantangan Transisi Energi: Implikasi Kebijakan Pasca Presidensi G20 Indonesia. Journal Of Tax Policy, Economics, And Accounting, 1(1), 16–34. https://doi.org/10.61261/taxpedia.v1i1.14
Herindrasti, S., Angelina, B., & Putriwinata, P. (2024). Pengembangan Kebijakan Energi Terbarukan di Indonesia, Vietnam, dan Laos. Sospol, 10(2), 154–172. https://doi.org/10.22219/jurnalsospol.v10i2.35634
Hsu, J.-Y., Wang, Y.-F., Lin, K.-C., Chen, M.-Y., & Hsu, J. H.-Y. (2020). Wind Turbine Fault Diagnosis and Predictive Maintenance through Statistical Process Control and Machine Learning. IEEE Access, 8, 23427–23439. https://doi.org/10.1109/ACCESS.2020.2968615
Indonesia.go.id. (24 Oktober 2024). Menggali Potensi Angin: Langkah Indonesia menuju Energi Terbarukan. https://indonesia.go.id/kategori/editorial/8719/menggali-potensi-angin-langkah-indonesia-menuju-energi-terbarukan?lang=1
Institute for Essential Services Reform. (5 November 2024). Peningkatan Bauran Energi Terbarukan Menggambarkan Semangat Dekarbonisasi. https://iesr.or.id/peningkatan-bauran-energi-terbarukan-menggambarkan-semangat-dekarbonisasi/
International Renewable Energy Agency. (2017). Renewable Energy Benefits: Leveraging Local Capacity for Onshore Wind. International Renewable Energy Agency. https://www.irena.org/publications/2017/Jun/Renewable-Energy-Benefits-Leveraging-Local-Capacity-for-Onshore-Wind
Kementerian Energi dan Sumber Daya Mineral Republik Indonesia. (2023). Wilayah Ini Miliki Potensi Energi Angin di Atas 100 MW. https://www.esdm.go.id/id/media-center/arsip-berita/wilayah-ini-miliki-potensi-energi-angin-di-atas-100-mw
Kementerian Koordinator Bidang Perekonomian Republik Indonesia. (2023). Pemerintah Terus Mendorong Percepatan Transisi Energi di dalam Negeri guna Mencapai Target Net Zero Emission pada 2060. https://www.ekon.go.id/publikasi/detail/4996/pemerintah-terus-mendorong-percepatan-transisi-energi-di-dalam-negeri-guna-mencapai-target-net-zero-emission-pada-2060
Munadi, R., Fuady, M., Noer, R., Kevin, M. A., Farrel, M. R., & Buraida. (2025). Towards Net-Zero-Energy Buildings in Tropical Climates: An IoT and EDGE Simulation Approach. Sustainability, 17, 9538. https://doi.org/10.3390/su17219538
Nurfauziah, N., & Sebayang, A. F. B. (2024, July). Strategi Pengembangan Mobil Listrik dalam Mendukung Sustainable Development Goals di Indonesia. In Bandung Conference Series: Economics Studies (Vol. 4, No. 3, pp. 636–645). https://doi.org/10.29313/bcses.v4i3.12760
Pahlevi, R., Thamrin, S., Ahmad, I., & Nugroho, F. B. (2024). Masa Depan Pemanfaatan Batubara sebagai Sumber Energi di Indonesia. Jurnal Energi Baru dan Terbarukan, 5(2), 50–60. https://doi.org/10.14710/jebt.2024.22973
Pandit, R. K., Astolfi, D., & Durazo Cardenas, I. (2023). A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines. Energies, 16, 1654. https://doi.org/10.3390/en16041654
Prabhavathi, K., Edwin, P. P. B., Arunadevi, M., & Shanthi, D. (2023). Design and Implementation of a Machine Learning-Based Wind Turbine Control System. E3S Web of Conferences, 387, 01008. https://doi.org/10.1051/e3sconf/202338701008
Prawiroredjo, K., Julian, E. S., Sulaiman, S., Novianto, S., & Moelyadi, Y. I. (2024). Penyuluhan Cara Menghemat dan Cara Aman Menggunakan Energi Listrik dalam Rumah Tangga. Jurnal Bakti Masyarakat Indonesia, 7(2), 273–282. https://doi.org/10.24912/jbmi.v7i2.30418
Rohendi, R., Sujaini, H., Yacoub, R. R., Sanjaya, B. W., & Purwoharjono. (2021). Desain Sistem Akuisisi Kecepatan Angin pada Menara SST berbasis IoT. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5(4), 739–746. https://doi.org/10.29207/resti.v5i4.3307
Salam, A. (2024). An Overview of Recent Wireless Technologies for IoT-Enabled Smart Grids. Journal of Electrical and Computer Engineering, 2024, Article 2568751. https://doi.org/10.1155/jece/2568751
Thiagarajan, S., Semmel, D. S., & Semmel, M. I. (1974). Instructional Development for Training Teachers of Exceptional Children: A Sourcebook. Indiana University, Center for Innovation in Teaching the Handicapped. https://files.eric.ed.gov/fulltext/ED090725.pdf
Yeh, C.-H., Lin, M.-H., Lin, C.-H., Yu, C.-E., & Chen, M.-J. (2019). Machine Learning for Long Cycle Maintenance Prediction of Wind Turbine. Sensors, 19(7), 1671. https://doi.org/10.3390/s19071671
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Proceedings of Castle
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
