IDEAS home Printed from https://ideas.repec.org/a/abx/journl/y2026id1006.html

Optimizing Energy Consumption at MAZ Using IoT Sensors and Neural Networks for Predictive Analysis

Author

Listed:
  • E. Polosko

  • O. Holda

Abstract

The energy intensity of the Belarusian mechanical engineering industry remains high – approximately 250 kWh per 1 BYR of output, making further improvements to energy efficiency strategically important. In the first half of 2025, MAZ OJSC achieved an energy savings rate of 6.9 %. The company still lacks an integrated real-time system that would analyze sensor data and predict equipment energy consumption for optimal mode planning and peak load reduction. This article presents an integrated model: IoT sensors collect data on power, vibration, and load, an LSTM neural network accurately forecasts energy consumption for several hours in advance, and an intelligent optimizer automatically redistributes production processes among favorable tariff zones. The system integrates with MAZ’s existing automated metering systems. The model will reduce energy consumption to 98.9 kWh/BYR with a phased implementation of the system in 2026, beginning with a pilot project in the second quarter of this year and achieving full effectiveness by 2027.

Suggested Citation

  • E. Polosko & O. Holda, 2026. "Optimizing Energy Consumption at MAZ Using IoT Sensors and Neural Networks for Predictive Analysis," Digital Transformation, Educational Establishment “Belarusian State University of Informatics and Radioelectronicsâ€, vol. 32(1).
    • Handle: RePEc:abx:journl:y:2026:id:1006
    DOI: 10.35596/1729-7648-2026-32-1-45-50
    as

    Download full text from publisher

    File URL: https://dt.bsuir.by/jour/article/viewFile/1006/381
    Download Restriction: no
    File URL: https://libkey.io/10.35596/1729-7648-2026-32-1-45-50?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Seljom, Pernille & Kvalbein, Lisa & Hellemo, Lars & Kaut, Michal & Ortiz, Miguel Muñoz, 2021. "Stochastic modelling of variable renewables in long-term energy models: Dataset, scenario generation & quality of results," Energy, Elsevier, vol. 236(C).
    2. Zhou, Xinlei & Lin, Wenye & Kumar, Ritunesh & Cui, Ping & Ma, Zhenjun, 2022. "A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption," Applied Energy, Elsevier, vol. 306(PB).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Yanli & Wang, Junyi & Liu, Liqi, 2024. "Physics-informed reinforcement learning for probabilistic wind power forecasting under extreme events," Applied Energy, Elsevier, vol. 376(PA).
    2. Yongjie Yang & Yulong Li & Yan Cai & Hui Tang & Peng Xu, 2024. "Data-Driven Golden Jackal Optimization–Long Short-Term Memory Short-Term Energy-Consumption Prediction and Optimization System," Energies, MDPI, vol. 17(15), pages 1-20, July.
    3. Jåstad, Eirik Ogner & Trotter, Ian M. & Bolkesjø, Torjus Folsland, 2022. "Long term power prices and renewable energy market values in Norway – A probabilistic approach," Energy Economics, Elsevier, vol. 112(C).
    4. Haizhou Fang & Hongwei Tan & Ningfang Dai & Zhaohui Liu & Risto Kosonen, 2023. "Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search," Sustainability, MDPI, vol. 15(9), pages 1-23, May.
    5. Cao, Wenqiang & Yu, Junqi & Chao, Mengyao & Wang, Jingqi & Yang, Siyuan & Zhou, Meng & Wang, Meng, 2023. "Short-term energy consumption prediction method for educational buildings based on model integration," Energy, Elsevier, vol. 283(C).
    6. Ali, Aliyuda & Aliyuda, Kachalla & Elmitwally, Nouh & Muhammad Bello, Abdulwahab, 2022. "Towards more accurate and explainable supervised learning-based prediction of deliverability for underground natural gas storage," Applied Energy, Elsevier, vol. 327(C).
    7. Lu, Jie & Zhang, Chaobo & Li, Junyang & Zhao, Yang & Qiu, Weikang & Li, Tingting & Zhou, Kai & He, Jianing, 2022. "Graph convolutional networks-based method for estimating design loads of complex buildings in the preliminary design stage," Applied Energy, Elsevier, vol. 322(C).
    8. Yin, Linfei & Qiu, Yao, 2022. "Neural network dynamic differential control for long-term price guidance mechanism of flexible energy service providers," Energy, Elsevier, vol. 255(C).
    9. Ulrich Joachim Frey & Karl-Kiên Cao & Shima Sasanpour & Jan Buschmann & Thomas Breuer, 2026. "The benefits of exploring a large scenario space for future energy systems," Nature Communications, Nature, vol. 17(1), pages 1-12, December.
    10. Seljom, Pernille & Rosenberg, Eva & Haaskjold, Kristina, 2024. "The effect and value of end-use flexibility in the low-carbon transition of the energy system," Energy, Elsevier, vol. 292(C).
    11. Jåstad, Eirik Ogner & Bolkesjø, Torjus Folsland, 2023. "Offshore wind power market values in the North Sea – A probabilistic approach," Energy, Elsevier, vol. 267(C).
    12. Qian, Tong & Chen, Xingyu & Xin, Yanli & Tang, Wenhu & Wang, Lixiao, 2022. "Resilient decentralized optimization of chance constrained electricity-gas systems over lossy communication networks," Energy, Elsevier, vol. 239(PB).
    13. Steven S. Salim & Stefan L. Luxembourg & Francesco Dalla Longa & Bob van der Zwaan, 2024. "From Retrofitting to Renewables: Navigating Energy Transition Pathways for European Residential Space Heating," Energies, MDPI, vol. 17(10), pages 1-28, May.
    14. Sprake, David & Monir, Shafiul & Vagapov, Yuriy & Bolam, Robert Cameron & Elcock, David, 2025. "Modelling and optimisation of shared renewable energy microgrid for proposed UK residential community," Energy, Elsevier, vol. 341(C).
    15. Kamil Misiurek & Tadeusz Olkuski & Janusz Zyśk, 2025. "Review of Methods and Models for Forecasting Electricity Consumption," Energies, MDPI, vol. 18(15), pages 1-27, July.
    16. Nikseresht, Ali & Amindavar, Hamidreza, 2024. "Energy demand forecasting using adaptive ARFIMA based on a novel dynamic structural break detection framework," Applied Energy, Elsevier, vol. 353(PA).
    17. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    18. Wu, Han & Liang, Yan & Heng, Jiani, 2023. "Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting," Applied Energy, Elsevier, vol. 339(C).
    19. Liang, Xuedong & Luo, Peng & Li, Xiaoyan & Wang, Xia & Shu, Lingli, 2023. "Crude oil price prediction using deep reinforcement learning," Resources Policy, Elsevier, vol. 81(C).
    20. Ghafoori, Mahdi & Abdallah, Moatassem & Kim, Serena, 2023. "Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system," Applied Energy, Elsevier, vol. 340(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:abx:journl:y:2026:id:1006. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ð ÐµÐ´Ð°ÐºÑ†Ð¸Ñ (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.