Laili, Clairina Elora (2025) Desain dan Implementasi Dashboard Manajemen Energi Berbasis Internet of Things dan Regresi Linier pada Gedung AJ Teknik Elektro ITS dengan Komunikasi Wi-Fi. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Manajemen energi merupakan langkah strategis dalam mengoptimalkan konsumsi energi untuk mendukung keberlanjutan lingkungan dan efisiensi operasional. Penelitian ini bertujuan untuk merancang sistem manajemen energi pada Gedung AJ Teknik Elektro ITS yang terintegrasi dengan dashboard Blynk berbasis Internet of Things (IoT). Penelitian ini mencakup perancangan desain, implementasi konektivitas dashboard Blynk dengan internet, serta analisis perbandingan efektivitas pengiriman data menggunakan Wi-Fi dibandingkan dengan pengukuran langsung. Selain itu, penelitian ini juga mengevaluasi tingkat akurasi Multivariate Linear Regression berganda dalam memprediksi konsumsi energi perangkat listrik seperti AC, lampu, dan peralatan lainnya. Hasil penelitian menunjukkan bahwa pembacaan data hasil pengukuran parameter yang telah terpasang dengan eror paling besar yaitu 2,734%. Hasil penelitian ini juga menunjukkan sistem manajemen energi berbasis IoT dengan metode Multivariate Linear Regression berganda dapat memprediksi konsumsi energi pada perangkat listrik di Gedung AJ. Model ini memiliki eror paling rendah pada peralatan AC sebesar 1,47%, peralatan lampu 18,51%, serta peralatan others sebesar 16,81%. Model regresi pertama dan kedua menunjukkan akurasi yang baik untuk memprediksi konsumsi energi pada AC tetapi memerlukan optimasi lebih lanjut untuk kategori lampu dan others dengan model prediksi yang lebih kompleks.
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Energy management is a strategic approach to optimizing energy consumption to support environmental sustainability and operational efficiency. This research aims to design an energy management system for the AJ Building of the Electrical Engineering Department at ITS, integrated with a Blynk dashboard based on the Internet of Things (IoT). The study includes the design framework, the implementation of Blynk dashboard connectivity with the internet, and an analysis comparing the effectiveness of data transmission using Wi-Fi versus direct measurements. Additionally, the study evaluates the accuracy of Multivariate Linear Regression in predicting the energy consumption of electrical devices such as air conditioners (AC), lights, and other appliances. The results indicate that the measured parameter readings installed in the system exhibit a maximum error rate of 2,734%. The study also demonstrates that the IoT-based energy management system using Multivariate Linear Regression can accurately predict energy consumption for electrical devices in the AJ Building. The model shows the lowest error for air conditioners at 1,47%, lighting devices at 18,51%, and other appliances at 16,81%. The first and second regression models exhibit good accuracy in predicting energy consumption for air conditioners but require further optimization for lighting and other categories using more complex predictive models.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Manajemen Energi, Linier Regression, Internet of Things, Wi-Fi, Energy Management, Regression Linier, Internet of Things, Wi-Fi |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001 Production of electric energy or power |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20201-(S1) Undergraduate Thesis |
| Depositing User: | Clairina Elora Laili |
| Date Deposited: | 30 Jan 2025 00:48 |
| Last Modified: | 30 Jan 2025 00:48 |
| URI: | http://repository.its.ac.id/id/eprint/117083 |
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