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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Teknosains Majalah Ilmiah Pengkajian Industri FIRM: Journal of Management Studies Berdikari : Jurnal Pengabdian kepada Masyarakat KAMI MENGABDI Jurnal of Information Technology and Society (JITS) Journal of Applied Science and Advanced Engineering
Muliadi, Jemie
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Aerospace Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation

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Journal : Journal of Applied Science and Advanced Engineering

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Analysis Of Prediction Of Electrical Power Use Outside Peak Load Of Apartment Building X Using The Long Short Term Memory (LSTM) Method: A Case Study of Apartment Building X Nazara, Meiman Zaro; Rofii, Ahmad; Muliadi, Jemie
Journal of Applied Science and Advanced Engineering Vol. 4 No. 1 (2026): Issue in Progress
Publisher : Master Program in Mechanical Engineering, Gunadarma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59097/jasae.v4i1.80

Abstract

Energy efficiency in residential high-rise buildings has become a critical issue in modern power management, particularly during off-peak periods (LWBP), which contribute significantly to daily electricity consumption. However, most existing studies have primarily focused on peak load forecasting, leaving limited exploration of electricity usage during off-peak hours. This study proposes a daily electricity consumption forecasting model for the off-peak period using the Long Short-Term Memory (LSTM) architecture, designed to capture long-term dependencies in time-series data. The dataset consists of one year of historical daily electricity consumption records from Apartment X. Data preprocessing included Min-Max normalization, time windowing, and partitioning into 80% training and 20% testing sets. Hyperparameter optimization was performed using Optuna, while model performance was evaluated using RMSE, MAE, MSE, and R² metrics. Experimental results demonstrate that the LSTM model effectively captured the temporal patterns of LWBP electricity consumption, achieving RMSE = 0.140, MAE = 0.109, MSE = 0.020, and R² = 0.537. These findings highlight the potential of LSTM as a decision-support tool for building energy management systems, enabling optimization of electricity usage during non-peak hours. Furthermore, this work provides opportunities for future research by integrating hybrid deep learning architectures (e.g., CNN-LSTM or Bi-LSTM) and incorporating external factors such as temperature, weather conditions, and occupant behavior to improve predictive accuracy in real-world applications.
Prediction of Peak Load Electricity Consumption in Apartment X Building Using Deep Learning with GRU Method Yusuf, Yusuf; Rofii, Ahmad; Muliadi, Jemie
Journal of Applied Science and Advanced Engineering Vol. 4 No. 1 (2026): Issue in Progress
Publisher : Master Program in Mechanical Engineering, Gunadarma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59097/jasae.v4i1.81

Abstract

This study presents a predictive framework for daily electricity consumption forecasting in Apartment X using a Recurrent Neural Network (RNN) model with the Gated Recurrent Unit (GRU) method. The dataset consists of daily electricity log sheets containing two main variables: Peak Load Time (WBP) and Off-Peak Load Time (LWBP). The preprocessing stage includes data cleaning, normalization using Min–Max Scaling, and sequence formation through a sliding window approach. The GRU architecture comprises two hidden layers, a dropout layer, and optimization using the Adam optimizer. The model’s performance was evaluated using MAE, RMSE, and R². The results show that the GRU model achieved an R² value of 0.623, indicating a good capability in capturing consumption patterns. This study contributes to energy forecasting studies in developing countries, emphasizing smart building energy management applications
Co-Authors Adhynugraha, Muhammad Ilham AHMAD ROFI'I Ahmad Rofii Alfaqih, Muhammad Subhan Anto Satriyo Nugroho, Anto Satriyo Arwanto, Arwanto Aryanti, Evy Aryanti Asmail, Asmail Budiarti, Dewi Habsari Ferianasari, Inneke Winda Hamdala, Marcia Rizky Hi Rauf, Siti Nuraini Kamaruddin, Muhammad Junaid Khana, Rajes MANURUNG, NICO DEMEUS HASOLOAN Megawanto, Fadli Cahya Montolalu, Ivan Adrian Mufit, Choirul Muhammad Sobirin Muryanto, Heri T. Nami, Osen Fili Nasrullah Armi Nazara, Meiman Zaro Nugraha, Yudha Aditya Octaviany, Siti Vivi Pamungkas, Lukas Sangka Pernando, Frengki Prayitno, Endarmadi A. Purwoko, Reza Yuridian Putra, Gustri Dede Rahayaan, Manuela Rima Melati Ruhdiat, Rudi Rusdianto Roestam Santoso, Kukuh Aris Saputra, Silvan Sinaga, Muktar Supriyadi, Muhamad Rodhi Tono, Marvin Tris Wan Sen, Tjong Wan Sen Wibowo, Singgih Satrio Yanis, Fauziah Yusuf Yusuf