{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T14:39:06Z","timestamp":1772548746638,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia","award":["IPP: 107-830-2025"],"award-info":[{"award-number":["IPP: 107-830-2025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Accurate electrical power prediction is increasingly critical in industrial smart manufacturing environments, where energy fluctuations and demand variability pose significant operational challenges under the industry 4.0 paradigm. Existing approaches often rely on simulated or secondary data and lack integration with industrial-grade communication protocols, limiting their practical applicability. Incorporating machine learning with real-time data collection is essential for progressing industrial predictive monitoring. This research presents a framework to forecast electrical power usage by utilizing the RS-485 protocol to enhance smart manufacturing processes. The dataset used was obtained from a power meter, recorded over a period of 135 min, resulting in 3100 data. Three learning methods\u2014Random Forest, Extra Trees, and XGBoost\u2014were analyzed, with XGBoost being further refined through PSO for tuning hyperparameters. The models were trained on datasets that included voltage, current, frequency, and power factor, and their effectiveness was evaluated using time-based predictions, standard metrics, and error distributions through cross-validation. The findings illustrate that the PSO-XGBoost consistently surpasses the default XGBoost baseline R2 of 0.5746, achieving MAE of 0.14 W, RMSE of 0.21 W, and R2 of 0.8355, representing improvements of 41.67% in MAE, 38.24% in RMSE, and 45.40% in R2. The RS-485 protocol enables seamless integration with existing industrial infrastructure, supporting anomaly detection and energy optimization aligned with Industry 4.0 interoperability objectives.<\/jats:p>","DOI":"10.3390\/info17030251","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T09:45:12Z","timestamp":1772531112000},"page":"251","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Electrical Power Prediction Using RS-485 Power Meter: A PSO-Optimized XGBoost Approach for Industrial Smart Manufacturing"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4994-3360","authenticated-orcid":false,"given":"Mulki Indana","family":"Zulfa","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Jenderal Soedirman University, Purbalingga 53371, Indonesia"}]},{"given":"Adhe Akbar","family":"Azanni","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Jenderal Soedirman University, Purbalingga 53371, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2267-6058","authenticated-orcid":false,"given":"Muhammad Syaiful","family":"Aliim","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Jenderal Soedirman University, Purbalingga 53371, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9551-2580","authenticated-orcid":false,"given":"Ari","family":"Fadli","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Gajah Mada University, Yogyakarta 55281, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3746-4274","authenticated-orcid":false,"given":"Waleed","family":"Ali","sequence":"additional","affiliation":[{"name":"Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh 21911, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0330-8372","authenticated-orcid":false,"given":"Talal A. A.","family":"Abdullah","sequence":"additional","affiliation":[{"name":"Interdisciplinary Research Center for Finance and Digital Economy, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kiasari, M., Ghaffari, M., and Aly, H.H. (2024). A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems. Energies, 17.","DOI":"10.3390\/en17164128"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2632","DOI":"10.1109\/TMECH.2024.3456250","article-title":"Embodied Intelligence Toward Future Smart Manufacturing in the Era of AI Foundation Model","volume":"30","author":"Ren","year":"2025","journal-title":"IEEE\/ASME Trans. 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