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IoT-Based Sensor System for Electricity Consumption Forecasting in Boarding Rooms Using Kalman Filter Algorithm Dewi, Dewi Humeira Amriah; Farid; Muhammad Fathur Rahman N; Riesa Krisna Astuti Sakir; Muh. Erdin; Nurfitri
Jurnal Teknologi Elekterika Vol. 22 No. 2 (2025): Nopember
Publisher : Jurusan Teknik Elektro Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/elekterika.v22i2.5760

Abstract

Humans demand electricity to conduct their daily tasks. Indonesia undergoes a yearly escalation in its electricity use. Moreover, customers encounter the difficulty of overestimating their electrical energy consumption, since they remain unaware of the power utilization linked to each frequently utilized electrical load and possess limited control over their electricity expenditure. The Kalman Filter Algorithm was utilized to estimate electricity usage via the execution of a research system. The Kalman Filter can forecast future states using minimal information. This system incorporates an IoT framework with a network communication module, the Raspberry Pi, which relays data to the database. The PZEM-004T sensor is utilized to gather data on electrical parameters from loads, including voltage, current, active power, and energy consumption. The electrical consumption was documented every 15 minutes over a duration of 60 days. The dataset was divided in an 80:20 ratio, allocating 80% for training and 20% for testing. RMSE, MSE, and MAPE are utilized to determine the accuracy metrics of each test. Additionally, the fan load is assessed in one evaluation, yielding an error percentage of 0.077% for the training data and 0.076% for the test data, determined using RMSE. The error percentage calculated using the MSE equation is 0.006% for the training data and 0.005% for the test data. The error percentage calculated by MAPE is 0.789% for the training dataset and 0.202% for the testing dataset. The findings indicate that the Kalman Filter prediction method is exceptionally proficient in forecasting electrical load consumption
Co-Authors Abdul Haris Sunubi Afrianti Agustira, Annisa Aldri Frinaldi Alwi Amin, Bani Andi Nur Aisyah Andi Sadapotto H. Andi Wahyuni arafah, Salmah Arianggara, Andi Wilda Asfar, Akbar Asmirinda Resa Asti Melinda Sapitri Bustami, Edwin Cayati Delia Desvianti Desi Ernita Amru Dewi Astria Faroek Dewi, Dewi Humeira Amriah Dewiyanti Dian Anggraini Noor Dina Oktaviana Dita Puteri , Ade edy Erdin, Muh. Ermin Ermin Ernawati Esse Puji Pawenrusi Farid Fatia, Dinda Framita Rahman Fries Pileman Soa'e Sigilipu Hasna Kamriana, Kamriana Kasumayanti, Erma Kusumaningrum, Rastri Lulu Jola Uktolseja M. Padli Ma'arif, Asraf Margiyanti, Norma Jeepi Marisa Meladina Mike Nurmalia Sari Mochammad Nuer Alim Qalby Muhammad Fathur Rahman N Muhammad Ramdhani, Muhammad Muhammad Yusuf Muhammad, Mahdir Munalika Nanda Pambudi Nurdjan, Nirwana Perawati Bte Abustang Putri, Rayhan rahma, St.Rahmawati Ramli, Rahmawati Renaldi M Rianti, Repa Riesa Krisna Astuti Sakir Ririn Safitri Riskiyatul Hasanah Roza, Nelli Safira Putri Sam Hermansyah Sari, Desi Pramita Sari, Desi Premita Sarmila Sigit Sitti Aisa St. Nur Alisa Sunah, M Dhany Al Syamsu Tang Tarigan, Renny Adelia Tella, Fitriyani Wilujeng, Ayu Rosanti Yuni Dhea Utari Zulfikar