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primary topic: Enhancing artificial neural network performance for energy efficiency in laboratories through principal component analysis

Enhancing artificial neural network performance for energy efficiency in laboratories through principal component analysis
type: AcademicArticle
type: Article
abstract: 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.
date: 2025
volume: 14
issue: 2
is part of: International Journal of Advances in Applied Sciences
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

Intelektual Pustaka Media Utama
type: Organization

International Journal of Advances in Applied Sciences
type: Collection
issn: 22528814

Mustofa Abi Hamid
type: Person
given name: Mustofa Abi
family name: Hamid

Radinal Fadli
type: Person
given name: Radinal
family name: Fadli

Desmira
type: Person
family name: Desmira

Muhammad Hakiki
type: Person
given name: Muhammad
family name: Hakiki

Norazhar Abu Bakar
type: Person
given name: Norazhar Abu
family name: Bakar

Affero Ismail
type: Person
given name: Affero
family name: Ismail

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primary topic: Enhancing artificial neural network performance for energy efficiency in laboratories through principal component analysis