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All Journal Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering)
Koko Friansa
Bandung Institute of Technology
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Comparasion of HVAC Energy Consumption Prediction in an Academic Building using LSTM and DNN Hadi Christian; Koko Friansa; Justin Pradipta; Irsyad Nashirul Haq; Edi Leksono
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 11 No 1 (2024): Jurnal Ecotipe, April 2024
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jurnalecotipe.v11i1.4488

Abstract

Energy consumption information is a collection of information obtained from datasets that is useful for making decisions for energy conservation. In this paper, we proposed a modern approach based on LSTM and DNN. While many researchers have employed these methods for predicting energy consumption, this paper seeks to compare their efficacy to determine which is superior. The comparative analysis in question employs accuracy metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) values. Furthermore, the accuracy metric outcomes indicate that the LSTM method surpasses the DNN approach in terms of the R-squared (R2) value, with respective scores of 0.931 and 0.782. Meanwhile, for other accuracy metrics, the DNN method outperforms LSTM. Nevertheless, the performance of the two proposed methods is excellent, as evidenced by the R-squared (R2) value exceeding 0.75, which aligns with modeling standards observed in numerous research studies.
Co-Authors Edi Leksono Hadi Christian Irsyad Nashirul Haq Justin Pradipta