Sustainable Energy Control and Optimization
ISSN: 3068-7330 (Online)
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TY - JOUR AU - Rinanto, Noorman AU - Wuri, Adinda Putri Asrining AU - Adhitya, Ryan Yudha AU - Ismail, Harun PY - 2025 DA - 2025/12/29 TI - Comparative Simulation of Kalman Filter and Moving Average on Siemens S7-1200 PLC-Based Loadcell Sensor Readings JO - Sustainable Energy Control and Optimization T2 - Sustainable Energy Control and Optimization JF - Sustainable Energy Control and Optimization VL - 1 IS - 2 SP - 67 EP - 76 DO - 10.62762/SECO.2025.538779 UR - https://www.icck.org/article/abs/SECO.2025.538779 KW - kalman filter KW - moving average filter KW - siemens S7-1200 PLC KW - load-cell KW - signal filtering AB - This work compared the effectiveness of the Kalman Filter and Moving Average Filter methods in minimizing noise and improving the stability of signal readings on a load-cell sensor simulation. The two filtering methods were applied to process the sensor data, to enhance both the precision and stability of the signal readings. According to the test results, the Kalman filter produced a lower average error of 0.0236, compared to 0.0244 when no filter was used, demonstrating its strong ability to reduce noise and signal fluctuations. On the other hand, the Moving Average Filter recorded a slightly higher error of 0.0238. Although it effectively smooths the signal, its performance is less reliable when dealing with higher levels of interference. Based on these findings, the Kalman Filter is considered more suitable for applications that require highly accurate and stable measurements, while the Moving Average Filter may be sufficient for environments with minimal noise. SN - 3068-7330 PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Rinanto2025Comparativ,
author = {Noorman Rinanto and Adinda Putri Asrining Wuri and Ryan Yudha Adhitya and Harun Ismail},
title = {Comparative Simulation of Kalman Filter and Moving Average on Siemens S7-1200 PLC-Based Loadcell Sensor Readings},
journal = {Sustainable Energy Control and Optimization},
year = {2025},
volume = {1},
number = {2},
pages = {67-76},
doi = {10.62762/SECO.2025.538779},
url = {https://www.icck.org/article/abs/SECO.2025.538779},
abstract = {This work compared the effectiveness of the Kalman Filter and Moving Average Filter methods in minimizing noise and improving the stability of signal readings on a load-cell sensor simulation. The two filtering methods were applied to process the sensor data, to enhance both the precision and stability of the signal readings. According to the test results, the Kalman filter produced a lower average error of 0.0236, compared to 0.0244 when no filter was used, demonstrating its strong ability to reduce noise and signal fluctuations. On the other hand, the Moving Average Filter recorded a slightly higher error of 0.0238. Although it effectively smooths the signal, its performance is less reliable when dealing with higher levels of interference. Based on these findings, the Kalman Filter is considered more suitable for applications that require highly accurate and stable measurements, while the Moving Average Filter may be sufficient for environments with minimal noise.},
keywords = {kalman filter, moving average filter, siemens S7-1200 PLC, load-cell, signal filtering},
issn = {3068-7330},
publisher = {Institute of Central Computation and Knowledge}
}
Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 
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