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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Teknika INVOTEK: Jurnal Inovasi Vokasional dan Teknologi Syntax Literate: Jurnal Ilmiah Indonesia KACANEGARA Jurnal Pengabdian pada Masyarakat JE-Unisla Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Engineering and Technology (INAJET) INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) IJORER : International Journal of Recent Educational Research Jurnal Teknik Elektro (JTE) Indonesian Journal of Multidiciplinary Research (IJoMR) Abimanyu: Journal of Community Engagement Tarbiyah Wa Ta'lim: Jurnal Penelitian Pendidikan dan Pembelajaran Jurnal Indonesia Sosial Teknologi Jurnal Elektronika dan Teknik Informatika Terapan IT-Edu: Jurnal Information Technology and Education
Unit Three Kartini
Universitas Negeri Surabaya
Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Transportation Other

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Hybrid KNN-LSTM Modeling for Short-Term Feeder Peak Load Forecasting Muhammad, Yasyfin Nur; Kartini, Unit Three; Peni, Hapsari
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v10i4.58191

Abstract

Load forecasting is important in power system planning and management. Accurate forecasting is key in maintaining the balance of energy supply and demand. This research develops a hybrid KNN-LSTM method for load forecasting using historical load and voltage data. KNN is used in finding local patterns and LSTM is used in capturing long-term patterns. The result is that the KNN-LSTM method provides MSE 30289.4952, RSME 174.0387, and MAE 98.9081. These results are better than the KNN and LSTM methods alone. In addition, by adding the voltage feature, the prediction result increases by 50.5%. Keywords: Load forecasting, KNN, LSTM, KNN-LSTM
Design of Forecasting Electrical Power of Ultra-Short-Term Solar Power Using the Hybrid Model K-Nearest Neighbors LSTM Yulianto, Tri Wahyu; Kartini, Unit Three; Suprianto, Bambang
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 7 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i7.1230

Abstract

For the application of renewable energy at the airport, the use of solar power requires certainty of the electricity produced. The certainty of electricity generated from solar power can be predicted using machine learning methods. Predictions made on PV electrical power output are based on historical data from direct measurements from solar PV parameters, including solar radiation and PV panel temperature. Various types of machine learning methods for predicting PV output power have been used in previous studies with different eval_uation values of prediction results. In this study, the author conducted a hybrid K-NN method with LSTM to predict the PV electrical power of solar PV output with solar radiation parameters and PV panel temperature. After making predictions using this method, excellent RSME results were obtained with a value of 0.015424830635781967. The results of the PV output power value graph in this prediction are also very good, where the predicted value is close to the value of the testing data or actual data.
Modeling of Fault Alarm Monitoring System on IoT-Based Control Panel Alarm Annunciator Saputra, Ramadhan Dwi; Haryudo, Subuh Isnur; Tjahyaningtijas, Raden Roro Hapsari Peni Agustin; Kartini, Unit Three
invotek Vol 24 No 1 (2024): INVOTEK: Jurnal Inovasi Vokasional dan Teknologi
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/invotek.v24i1.1175

Abstract

The existence of substation operators is essential for monitoring substation equipment and operating control rooms. In addition, substation operators also have other tasks, such as inspecting substation equipment in the switchyard or in-service inspection (Level 1 inspection). This condition is less than ideal because the operator cannot monitor the control room continuously while in the switchyard. The control panel at the substation is equipped with an alarm annunciator, which is auxiliary equipment that serves to provide warning signs to the operator regarding which protection functions are working. Therefore, it is necessary to have a monitoring system that can provide real-time information about the condition of the control panel alarm annunciator. This research models a fault alarm monitoring system that can provide notification messages according to the type of fault automatically through the Telegram application. The system test results show the average value of 100 test data on the five types of fault, resulting in an accuracy rate of 100%, a precision rate of 100%, a recall rate of 100%, an error rate of 0%, and an F1-score rate of 100%. This is because all test data on the five types of fault were detected correctly, and no test data from other types of fault was detected as the five types of fault. Based on these averages, it can be concluded that using a confusion matrix to measure the performance of the fault alarm monitoring system on the IoT-based control panel annunciator alarm shows excellent system performance results.
Design of an IoT-Based Automatic Switching and Monitoring System for Hybrid Power Plants Aguska, Anggi; Haryudo, Subuh Isnur; Kartini, Unit Three; Rohman, Miftahur
invotek Vol 24 No 1 (2024): INVOTEK: Jurnal Inovasi Vokasional dan Teknologi
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/invotek.v24i1.1179

Abstract

Hybrid power generation, a power plant that combines two or more plants, continues to grow along with technological advances. The performance of these power plants relies heavily on effective switching and monitoring systems. Monitoring data is critical in maintenance scheduling, preventive intervention, and the timely identification and assessment of environmental changes. One of the switching and monitoring technologies integrated with the Internet is the Internet of Things (IoT) technology. This study introduces a system design capable of wirelessly performing switching operations and transmitting real-time data to a hybrid power plant monitoring system through an application. Test results demonstrate that the system successfully executes automatic switching between the hybrid power plant and the PLN electricity grid based on accumulator voltage thresholds. The monitoring data analysis reveals MAPE values of 2.959% and 3.577% for the voltage and current of the hybrid power plant, and a MAPE of 1% for the accumulator voltage. The voltage and load current readings also exhibit MAPEs of 0.604% and 8.625%. Based on the test results, it can be concluded that this device shows the ability of the system to automate the switching of resources to the load and monitor the hybrid power plant very well, with the smallest MAPE value achieved of 0.604%.
Co-Authors Achmad Imam Agung Achmad Imam Agung Adam Maulana Adi Reski Ariangga Aditya Prapanca Aguska, Anggi Akbar Tahir Kalbii Amarulloh, Ilham Anjar Novian Asto, I Gusti Putu At - Thariq Ramadhan Ayusta Lukita Wardani Bambang Suprianto . Budiarta, Mohammad Erwin DEDDY PUTRA ARDYANSYAH DWI ARDIANTO Dwikky Sucahyo Putra DZIKRI MUHAJIR EL FAHMI Edy Sulistiyo EKA PRASETYO HIDAYAT Endryansyah Endryansyah Farid Baskoro Fendi Achmad Feri Rohman Syah Ghifari Fikri Yuviyanto Habbib Rakhasiwi Aminulloh Hapsari Peni Hernanda Setiawan I Gusti Putu Asto Buditjahjanto Ibrohim Ichwan Dwi Wahyu Hermanto Ilham Amarulloh Ilham Cahyo Wibowo Aji Ilham Farisi Almadani Indra Iskandar Joko . Joko .Joko Joko Joko Joko Joko Kevin Pranata Putra Khoirul Fadli Krisna Taufik Brilliansyah Kristanto, Andika Wisnu Adam Kukuh Eko Purwantoro Lailil Ika Wardani Lilik Anifah Lusia Rakhmawati M. Nanda Tri Maulana Ridwan Mahendra Widyartono Mardika Wahyu Kristanto MASVIKI AGAM Maulana Rizki Aditama Mirza Wahyu Purnama MOCH. NUR ADIWANA Mochammad Iqbal Firmansyah Muhammad Fathoni Muhammad Helmy Anjab Muhammad Mujiburrahman Muhammad Rizka Ardiansyah Muhammad, Yasyfin Nur Mulya Adi Prasetiya Nining Widyah Kusnanik Nofianto Sugiarto Novian Zainun Qorif Putera Nur Kholis Nurhayati Nurhayati Nurwijayanti Pamungkas, Ivan Fahrezi Puguh Ady Mahendra Puput Wanarti Rusimamto Putra Adi Wicaksono Putri, Tiris Mega Rani Fajriyah Islamiyati Asfah Rifqi Firmansyah Rifqi Firmansyah, Rifqi Rizqi Rizal Dharmawan Roesita Dekakovi Tauba Setyawan Rohman, Miftahur Rois Alfikri RR. Ella Evrita Hestiandari S. Suparji Saifudin Saifudin Saputra, Ramadhan Dwi Sari Cahyaningtias Septian, Bahrul Anas Subuh Isnur Haryudo Suprianto Suprianto Syamsul Muarif Tedy Muhammadhy Tjahyaningtijas, Raden Roro Hapsari Peni Agustin Tri Rijanto Tri Wrahatnolo Tulende, James ULIN NIKMATUL CHOIROH W. Wasis Wahyu Tri Handoko WELBI RENALDI SUKRISNA widi . aribowo widi aribowo Widi Aribowo Widi Ariwibowo Wildan Arif Billahi WRAHATNOLO, TRI Yanuarius Kristian Wibisono Yuli Sutoto Nugroho Yulianto, Tri Wahyu Yusuf Rony Rony Yusuf, M. Yusuf Isbakhtiar