Artificial Intelligence Berbasis Deep Learning untuk Prediksi Efisiensi Energi pada Sistem WFA Menggunakan Data Sintetis

Dara Sawitri

doi:10.35447/jitekh.v14i2.1468

Authors

Dara Sawitri Program Studi Teknik Elektro, Fakultas Teknik dan Komputer, Universitas Harapan Medan, Medan, Indonesia

DOI:

https://doi.org/10.35447/jitekh.v14i2.1468

Keywords:

Artificial Intelligence, Deep Learning, Energy Efficiency, , Work from Anywhere, Synthetic Data

Abstract

The implementation of the Work from Anywhere (WFA) system has shifted electricity consumption patterns from centralized office environments to distributed locations such as homes, personal devices, co-working spaces, and internet-based digital services. In Indonesia in 2026, this phenomenon has become increasingly visible due to the growth of online activities and household electronic device usage, which significantly affect national electricity demand. In practice, several institutions and universities have started facing challenges in measuring energy efficiency because electricity consumption is no longer concentrated in a single location. This study proposes an Artificial Intelligence-based deep learning approach to predict energy efficiency levels in WFA environments using multivariate synthetic data. The dataset was constructed from several variables, including daily working duration, number of active devices, room temperature, occupancy level, air conditioning usage, internet connection quality, electricity tariffs, and electricity emission intensity. Four models were evaluated, namely Random Forest as the baseline model, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a lightweight Transformer model. Simulation results indicate that the lightweight Transformer achieved the best performance with an MAE of 0.041, RMSE of 0.057, MAPE of 5.31%, and a coefficient of determination (R²) of 0.928. The evaluation values were obtained from simulation results of the models using synthetic WFA system data. Overall, the findings demonstrate that deep learning approaches have strong potential to support data-driven monitoring systems and decision-making processes for improving energy efficiency in modern digital working environments

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References

R. Morcillo-Jimenez, J. Mesa, J. Gómez-Romero, M. A. Vila, and M. J. Martin-Bautista, “Deep learning for prediction of energy consumption: an applied use case in an office building,” Applied Intelligence, vol. 54, no. 7, pp. 5813–5825, Apr. 2024, doi: 10.1007/s10489-024-05451-9.

S. A. Nabavi, N. H. Motlagh, M. A. Zaidan, A. Aslani, and B. Zakeri, “Deep Learning in Energy Modeling: Application in Smart Buildings with Distributed Energy Generation,” IEEE Access, vol. 9, pp. 125439–125461, 2021, doi: 10.1109/ACCESS.2021.3110960.

K. Elhabyb, A. Baina, M. Bellafkih, and A. F. Deifalla, “Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings,” Int. J. Energy Res., vol. 2024, 2024, doi: 10.1155/2024/6812425.

D. Sawitri, “Paradigma Smart City Dalam Memanfaatkan Potensi Big Data,” vol. 11, no. 4, pp. 271–282, 2024, [Online]. Available: http://jurnal.mdp.ac.id

M. Tzelepi et al., “Deep Learning for Energy Time-Series Analysis and Forecasting,” Jun. 2023, [Online]. Available: http://arxiv.org/abs/2306.09129

R. Olu-Ajayi and H. Alaka, “Building energy consumption prediction using deep learning.”

D. Sawitri, “JISA (Jurnal Informatika dan Sains) Cloud Computing Technology in Supporting the Implementation of One Data Indonesia,” 2024.

D. Sawitri, “PERAN DEEP LEARNING DAN BIG DATA DALAM MENDEKTEKSI MASALAH KEUANGAN,” Jurnal Teknologi Informasi, vol. 6, no. 1, 2025, doi: 10.46576/djtechno.

A. B. Owolabi, A. Yahaya, M. Amir, A. O. Yakub, M. Kavgic, and D. Suh, “Predicting energy consumption of building clusters at the design stage using machine learning models,” Ain Shams Engineering Journal, vol. 16, no. 8, Aug. 2025, doi: 10.1016/j.asej.2025.103481.

Y. Duan, “A Novel Interval Energy-Forecasting Method for Sustainable Building Management Based on Deep Learning,” Sustainability (Switzerland), vol. 14, no. 14, Jul. 2022, doi: 10.3390/su14148584.

O. Bouhamed, M. Dissem, M. Amayri, and N. Bouguila, “Transformer-based deep probabilistic network for load forecasting,” Eng. Appl. Artif. Intell., vol. 152, Jul. 2025, doi: 10.1016/j.engappai.2025.110781.

A. Binbusayyis and M. Sha, “Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism,” Heliyon, vol. 11, no. 1, Jan. 2025, doi: 10.1016/j.heliyon.2024.e41507.

F. Saeed, A. Rehman, H. A. Shah, M. Diyan, C. Jie, and J.-M. Kang, “SmartFormer: Graph-Based Transformer Model for Energy Load Forecasting.” [Online]. Available: https://ssrn.com/abstract=4893923

S. Moveh, E. A. Merchán-Cruz, M. Abuhussain, Y. A. Dodo, S. Alhumaid, and A. H. Alhamami, “Deep Learning Framework Using Transformer Networks for Multi Building Energy Consumption Prediction in Smart Cities,” Energies (Basel)., vol. 18, no. 6, Mar. 2025, doi: 10.3390/en18061468.

T. A. Woldegiyorgis et al., “Harnessing Artificial Intelligence to improve building performance and energy use: innovations, challenges, and future perspectives,” Energy Informatics, vol. 8, no. 1, Dec. 2025, doi: 10.1186/s42162-025-00589-3.

R. Mathumitha, P. Rathika, and K. Manimala, “Intelligent deep learning techniques for energy consumption forecasting in smart buildings: a review,” Artif. Intell. Rev., vol. 57, no. 2, Feb. 2024, doi: 10.1007/s10462-023-10660-8.

Y. Wang, “Application of Deep Learning Model in Building Energy Consumption Prediction,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/4835259.

A. Kala, O. Torkul, T. Tunacan Yıldız, and I. H. Selvi, “Early Prediction of Students’ Performance Through Deep Learning: A Systematic and Bibliometric Literature Review,” Mar. 28, 2025, Sakarya University. doi: 10.35377/saucis...1635558.

A. Nanjar, R. E. Saputro, and B. Berlilana, “Machine Learning and Deep Learning Approaches for Energy Prediction: A Systematic Literature Review,” sinkron, vol. 8, no. 4, pp. 2603–2614, Nov. 2024, doi: 10.33395/sinkron.v8i4.14208.

J. Huang and S. Kaewunruen, “Forecasting Energy Consumption of a Public Building Using Transformer and Support Vector Regression,” Energies (Basel)., vol. 16, no. 2, Jan. 2023, doi: 10.3390/en16020966.

R. Mathumitha, P. Rathika, and K. Manimala, “Intelligent deep learning techniques for energy consumption forecasting in smart buildings: a review,” Artif. Intell. Rev., vol. 57, no. 2, Feb. 2024, doi: 10.1007/s10462-023-10660-8.

D. Sawitri, K. D. April, M. Sayuti, G. Ramdahan, and B. Suwanda, “IoT-Based Cassava Slicing Machine Using ESP32 with Smartphone Monitoring,” Jurnal Nasional Teknik Elektro, vol. 15, no. 1, 2026, doi: 10.25077/jnte.v15n1.1440.2026.

G. Chen et al., “A Systematic Review of Building Energy Consumption Prediction: From Perspectives of Load Classification, Data-Driven Frameworks, and Future Directions,” Mar. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/app15063086.

L. Moosbrugger, V. Seiler, G. Huber, and P. Kepplinger, “Improve Load Forecasting in Energy Communities through Transfer Learning using Open-Access Synthetic Profiles,” Jul. 2024, [Online]. Available: http://arxiv.org/abs/2407.08434

D. Sawitri, “Big Data Challenges And Opportunities In The Development Of Digital Technology,” Informatika dan Sains, vol. 14, no. 02, p. 2024, doi: 10.58471/infosains.v14i02.