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

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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
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    References listed on IDEAS

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