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All Journal Journal of Robotics and Control (JRC)
Toan, Nguyen Duc
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Integration of Modbus-Ethernet Communication for Monitoring Electrical Power Consumption, Temperature, and Humidity Le, Long Ho; Ngo, Thanh Quyen; Toan, Nguyen Duc; Nguyen, Chi Cuong; Phong, Bui Hong
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22456

Abstract

Effective management of electrical energy requires monitoring, controlling, and storing parameters gathered from power measurement devices including voltage, current, temperature, and humidity. This assessment of the quality of electrical energy is essential for management organizations, power companies, and individual consumers to develop efficient electricity usage plans. Based on the requirement, we proposed a hardware implementation for data collection and online communication software integrated with a system for collecting data on consumption of electrical energy. The EM115-Mod CT multifunction industrial meters by FINECO, the KLEA 220P three-phase multifunction meter by KLEMSAN, and the ME96SS–ver.B by MITSUBISHI are involved. Finally, the collected data of electrical consumption, temperature, and humidity can be stored on an SD card, transmitted to the cloud for real-time monitoring on mobile devices, and facilitated by the ESP-WROOM-32 microcontroller from Espressif system.
Integration of Modbus-Ethernet Communication for Monitoring Electrical Power Consumption, Temperature, and Humidity Le, Long Ho; Ngo, Thanh Quyen; Toan, Nguyen Duc; Nguyen, Chi Cuong; Phong, Bui Hong
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22456

Abstract

Effective management of electrical energy requires monitoring, controlling, and storing parameters gathered from power measurement devices including voltage, current, temperature, and humidity. This assessment of the quality of electrical energy is essential for management organizations, power companies, and individual consumers to develop efficient electricity usage plans. Based on the requirement, we proposed a hardware implementation for data collection and online communication software integrated with a system for collecting data on consumption of electrical energy. The EM115-Mod CT multifunction industrial meters by FINECO, the KLEA 220P three-phase multifunction meter by KLEMSAN, and the ME96SS–ver.B by MITSUBISHI are involved. Finally, the collected data of electrical consumption, temperature, and humidity can be stored on an SD card, transmitted to the cloud for real-time monitoring on mobile devices, and facilitated by the ESP-WROOM-32 microcontroller from Espressif system.
Improving Short-Term Electricity Load Forecasting Accuracy Using the Ghost Convolutional Neural Network Model Tuan, Nguyen Anh; Toan, Nguyen Duc
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26562

Abstract

Short-Term Load Forecasting (STLF) is essential for maintaining grid stability and optimizing operational efficiency in modern energy systems. While traditional Convolutional Neural Networks (CNNs) can extract local temporal features, they often struggle with capturing long-term dependencies and demand high computational resources. This study proposes a novel application of the Ghost Convolutional Neural Network (GhostCNN)—initially designed for image processing—to time-series electricity load forecasting. GhostCNN significantly reduces model complexity while preserving forecasting accuracy by generating redundant temporal features through lightweight linear operations. The model is trained and evaluated on a real-world electricity load dataset from Ho Chi Minh City, containing 13,440 hourly observations (~1.5 years). A comprehensive hyperparameter tuning strategy is applied, covering kernel size, Ghost ratio, sequence length, batch size, and learning rate. The model's performance is benchmarked against MLP, CNN, and LSTM architectures. GhostCNN achieves the lowest Mean Absolute Percentage Error (MAPE) of 1.15%, outperforming CNN (1.27%), MLP (1.67%), and LSTM (7.3%). Furthermore, GhostCNN reduces inference time by approximately 40% and decreases parameter count by ~45% compared to standard CNNs, affirming its suitability for real-time smart grid deployment. These results demonstrate that GhostCNN provides a robust, scalable, and efficient solution for accurate short-term electricity load forecasting in dynamic and resource-constrained environments.
Co-Authors Le, Long Ho Ngo, Thanh Quyen Nguyen Anh Tuan Nguyen, Chi Cuong Phong, Bui Hong