Desmira, Norazhar Abu Bakar, Mustofa Abi Hamid, Muhammad Hakiki, Affero Ismail, Radinal Fadli
This study investigates energy efficiency challenges during laboratory activities. Inefficient energy use in the practicum phase remains a critical issue, prompting the exploration of innovative forecasting models. This research employs artificial neural network (ANN) models integrated with principal component analysis (PCA) to predict energy consumption and optimize usage. The findings reveal that PCA components, including eigenvalues, eigenvectors, and matrix covariance values, significantly influence the ANN model's performance in forecasting energy production. The ANN training achieved a high correlation coefficient (R=1) with a mean squared error (MSE) of 0.045931 after 200,000 epochs, demonstrating the model's robustness. While testing results showed a moderate correlation (R=0.46169), the models demonstrated potential for refinement and scalability. This integration of ANN and PCA models provides a reliable framework for accurately forecasting energy usage, offering an effective strategy to enhance energy efficiency in laboratory settings. By optimizing energy consumption, this approach has the potential to reduce operational costs and environmental impact. The strong performance metrics highlight the practical utility of these models in educational contexts, contributing to sustainable energy management and better resource allocation. Furthermore, the reduction in energy-related environmental impacts underscores the broader applicability of these models for fostering sustainable development in similar contexts. © 2025, Intelektual Pustaka Media Utama. All rights reserved.
Department of Electrical Engineering Vocational Education, Universitas Sultan Ageng Tirtayasa, Serang, Indonesia; Center for Vocational and Technical Education and Training (Voctech), Yogyakarta, Indonesia; Department of Electrical and Electronics Engineering, Universiti Teknologi Petronas, Seri Iskandar, Malaysia; Department of Technological and Vocational Education, Graduate School, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia; Department of Information Technology Education, Universitas Negeri Surabaya, Surabaya, Indonesia; Department of Engineering Education, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia; UNESCO-UNEVOC International Centre for Technical and Vocational Education and Training, Bonn, Germany; Department of Information Technology Education, Universitas Lampung, Bandar Lampung, Indonesia
