Performance Comparison of Neural Network and Long Short-Term Memory for Forecasting Power Usage Effectiveness (PUE) in Data Center
Keywords:
ANN, Data Center, Energy Efficiency, LSTM, PUE
Abstract
Energy efficiency in data center operations has become a critical issue in the digital era, including for public utilities such as Regional Drinking Water Companies (PDAM). One of the key indicators of data center efficiency is Power Usage Effectiveness (PUE), with PDAM recording a daily average of 2.11. Significant spikes in PUE values during certain months suggest operational dynamics that are not yet fully understood, highlighting the need for a comprehensive analysis of the contributing factors. This study employs Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) algorithms to forecast PUE values. The research process includes data understanding, preprocessing, modeling, evaluation, and forecasting, conducted under three time-based split scenarios: 60%:20%:20%, 40%:20%:40%, and 20%:20%:60%. Among these, the third scenario demonstrated the best performance. The ANN model achieved a Mean Absolute Percentage Error (MAPE) of 10.10%, a Root Mean Square Error (RMSE) of 0.2565, and an accuracy of 85.34%. Meanwhile, the LSTM model recorded a MAPE of 10.62%, RMSE of 0.2600, and accuracy of 85.14%. These results fall within the 10%–20% error range, indicating reliable forecasting performance. The predicted PUE values were 2.38 for ANN and 1.85 for LSTM, both categorized as “Average” in terms of energy efficiency, suggesting room for improvement. The highest energy loads were attributed to cooling systems, specifically air conditioning (AC), air conditioning and mechanical ventilation (ACMV), and uninterruptible power supply (UPS) systems. Therefore, future infrastructure optimization efforts should prioritize improvements in these areas
Downloads
Download data is not yet available.
References
[1] laureguialzo, . Enrique, “PUE: The Green Grid metric for evaluating the energy efficiency in DC (Data Center). Measurement method using the Power Demand.,” 2011.
[2] V. Avelar, S. Electric, D. Azevedo, A. French, and E. Network Power, “PUETM: A COMPREHENSIVE EXAMINATION OF THE METRIC EDITORS,” 2012.
[3] A. Kumar Jain, “Predictive Analytics-Driven Management of Data Center Lifecycles,” JETIR2506882 Journal of Emerging Technologies and Innovative Research, vol. 12, 2025, [Online]. Available: https://learn.microsoft.com/en-us/azure/architecture/data-science-process/lifecycle
[4] O. Ilmiah Guru Besar Institut Teknologi Bandung and P. Kridanto Surendro, “FORUM GURU BESAR,” 2024. [Online]. Available: www.itbpress.id
[5] M. Ilham et al., “ANALISA BEBAN PENDINGIN PADA RUANG DATA CENTER DI PT BSH,” Jurnal Teknologi Industri, vol. 12, no. 1, 2023.
[6] “Perpres Nomor 22 Tahun 2017,” 2017.
[7] H. A. Ounifi, A. Gherbi, and N. Kara, “Deep machine learning-based power usage effectiveness prediction for sustainable cloud infrastructures,” Sustainable Energy Technologies and Assessments, vol. 52, Aug. 2022, doi: 10.1016/j.seta.2022.101967.
[8] J. Bi, S. Li, H. Yuan, Z. Zhao, and H. Liu, “Deep Neural Networks for Predicting Task Time Series in Cloud Computing Systems,” 2019, [Online]. Available: https://github.com/google/cluster-data
[9] K. Saranraj and C. M. Liu, “Enhancing Power Efficiency Prediction in Small-Scale Data Centers using Attention-Based Deep Neural Networks,” in 2023 IEEE 5th International Conference on Architecture, Construction, Environment and Hydraulics, ICACEH 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 44–49. doi: 10.1109/ICACEH59552.2023.10452493.
[10] M. Y. Anshori, V. Asy’Ari, T. Herlambang, and I. W. Farid, “Forecasting occupancy rate using neural network at Hotel R,” in 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), 2023, pp. 347–351. doi: 10.1109/ICAMIMIA60881.2023.10427752.
[11] T. Deepika and P. Prakash, “Power consumption prediction in cloud data center using machine learning,” International Journal of Electrical and Computer Engineering, vol. 10, no. 2, pp. 1524–1532, 2020, doi: 10.11591/ijece.v10i2.pp1524-1532.
[12] H. Dhake, Y. Kashyap, and P. Kosmopoulos, “Algorithms for Hyperparameter Tuning of LSTMs for Time Series Forecasting,” Remote Sens (Basel), vol. 15, no. 8, Apr. 2023, doi: 10.3390/rs15082076.
[13] A. W. Saputra, A. P. Wibawa, U. Pujianto, A. B. Putra Utama, and A. Nafalski, “LSTM-based Multivariate Time-Series Analysis: A Case of Journal Visitors Forecasting,” ILKOM Jurnal Ilmiah, vol. 14, no. 1, pp. 57–62, Apr. 2022, doi: 10.33096/ilkom.v14i1.1106.57-62.
[14] M. Hanasov, M. Wolter, and E. Glende, “Time Series Data Splitting for Short-Term Load Forecasting,” 2022.
[15] J. Han, J. Pei, and H. Tong, “Data Mining: Concepts and Techniques,” 2023.
[16] N. Khatun, “Applications of Normality Test in Statistical Analysis,” Open J Stat, vol. 11, no. 01, pp. 113–122, 2021, doi: 10.4236/ojs.2021.111006.
[17] A. Chernikov, C. W. Tan, P. Montero-Manso, and C. Bergmeir, “FRANS: Automatic Feature Extraction for Time Series Forecasting,” Sep. 2022, [Online]. Available: http://arxiv.org/abs/2209.07018
[18] N. P. Nur Fauzi, S. Khomsah, and A. D. Putra Wicaksono, “Penerapan Feature Engineering dan Hyperparameter Tuning untuk Meningkatkan Akurasi Model Random Forest pada Klasifikasi Risiko Kredit,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 12, no. 2, pp. 251–262, Apr. 2025, doi: 10.25126/jtiik.2025128472.
[19] F. Chollet, Deep Learning with Python. 2021.
[20] N. Alifiya, S. Putri, D. E. Ratnawati, and B. Rahayudi, “Prediksi Kebangkrutan Menggunakan Metode Backpropagation (Studi Kasus: Perseroan Terbatas Terdaftar Pada Bursa Efek Indonesia),” vol. 3, no. 3, pp. 2548–964, 2019, [Online]. Available: http://j-ptiik.ub.ac.id
[21] C. Tzagkarakis, P. Charalampidis, S. Roubakis, A. Fragkiadakis, and S. Ioannidis, “Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments,” Sep. 2022, [Online]. Available: http://arxiv.org/abs/2206.07784
REFERENCES
[1] laureguialzo, . Enrique, “PUE: The Green Grid metric for evaluating the energy efficiency in DC (Data Center). Measurement method using the Power Demand.,” 2011.
[2] V. Avelar, S. Electric, D. Azevedo, A. French, and E. Network Power, “PUETM: A COMPREHENSIVE EXAMINATION OF THE METRIC EDITORS,” 2012.
[3] A. Kumar Jain, “Predictive Analytics-Driven Management of Data Center Lifecycles,” JETIR2506882 Journal of Emerging Technologies and Innovative Research, vol. 12, 2025, [Online]. Available: https://learn.microsoft.com/en-us/azure/architecture/data-science-process/lifecycle
[4] O. Ilmiah Guru Besar Institut Teknologi Bandung and P. Kridanto Surendro, “FORUM GURU BESAR,” 2024. [Online]. Available: www.itbpress.id
[5] M. Ilham et al., “ANALISA BEBAN PENDINGIN PADA RUANG DATA CENTER DI PT BSH,” Jurnal Teknologi Industri, vol. 12, no. 1, 2023.
[6] “Perpres Nomor 22 Tahun 2017,” 2017.
[7] H. A. Ounifi, A. Gherbi, and N. Kara, “Deep machine learning-based power usage effectiveness prediction for sustainable cloud infrastructures,” Sustainable Energy Technologies and Assessments, vol. 52, Aug. 2022, doi: 10.1016/j.seta.2022.101967.
[8] J. Bi, S. Li, H. Yuan, Z. Zhao, and H. Liu, “Deep Neural Networks for Predicting Task Time Series in Cloud Computing Systems,” 2019, [Online]. Available: https://github.com/google/cluster-data
[9] K. Saranraj and C. M. Liu, “Enhancing Power Efficiency Prediction in Small-Scale Data Centers using Attention-Based Deep Neural Networks,” in 2023 IEEE 5th International Conference on Architecture, Construction, Environment and Hydraulics, ICACEH 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 44–49. doi: 10.1109/ICACEH59552.2023.10452493.
[10] M. Y. Anshori, V. Asy’Ari, T. Herlambang, and I. W. Farid, “Forecasting occupancy rate using neural network at Hotel R,” in 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), 2023, pp. 347–351. doi: 10.1109/ICAMIMIA60881.2023.10427752.
[11] T. Deepika and P. Prakash, “Power consumption prediction in cloud data center using machine learning,” International Journal of Electrical and Computer Engineering, vol. 10, no. 2, pp. 1524–1532, 2020, doi: 10.11591/ijece.v10i2.pp1524-1532.
[12] H. Dhake, Y. Kashyap, and P. Kosmopoulos, “Algorithms for Hyperparameter Tuning of LSTMs for Time Series Forecasting,” Remote Sens (Basel), vol. 15, no. 8, Apr. 2023, doi: 10.3390/rs15082076.
[13] A. W. Saputra, A. P. Wibawa, U. Pujianto, A. B. Putra Utama, and A. Nafalski, “LSTM-based Multivariate Time-Series Analysis: A Case of Journal Visitors Forecasting,” ILKOM Jurnal Ilmiah, vol. 14, no. 1, pp. 57–62, Apr. 2022, doi: 10.33096/ilkom.v14i1.1106.57-62.
[14] M. Hanasov, M. Wolter, and E. Glende, “Time Series Data Splitting for Short-Term Load Forecasting,” 2022.
[15] J. Han, J. Pei, and H. Tong, “Data Mining: Concepts and Techniques,” 2023.
[16] N. Khatun, “Applications of Normality Test in Statistical Analysis,” Open J Stat, vol. 11, no. 01, pp. 113–122, 2021, doi: 10.4236/ojs.2021.111006.
[17] A. Chernikov, C. W. Tan, P. Montero-Manso, and C. Bergmeir, “FRANS: Automatic Feature Extraction for Time Series Forecasting,” Sep. 2022, [Online]. Available: http://arxiv.org/abs/2209.07018
[18] N. P. Nur Fauzi, S. Khomsah, and A. D. Putra Wicaksono, “Penerapan Feature Engineering dan Hyperparameter Tuning untuk Meningkatkan Akurasi Model Random Forest pada Klasifikasi Risiko Kredit,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 12, no. 2, pp. 251–262, Apr. 2025, doi: 10.25126/jtiik.2025128472.
[19] F. Chollet, Deep Learning with Python. 2021.
[20] N. Alifiya, S. Putri, D. E. Ratnawati, and B. Rahayudi, “Prediksi Kebangkrutan Menggunakan Metode Backpropagation (Studi Kasus: Perseroan Terbatas Terdaftar Pada Bursa Efek Indonesia),” vol. 3, no. 3, pp. 2548–964, 2019, [Online]. Available: http://j-ptiik.ub.ac.id
[21] C. Tzagkarakis, P. Charalampidis, S. Roubakis, A. Fragkiadakis, and S. Ioannidis, “Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments,” Sep. 2022, [Online]. Available: http://arxiv.org/abs/2206.07784
[2] V. Avelar, S. Electric, D. Azevedo, A. French, and E. Network Power, “PUETM: A COMPREHENSIVE EXAMINATION OF THE METRIC EDITORS,” 2012.
[3] A. Kumar Jain, “Predictive Analytics-Driven Management of Data Center Lifecycles,” JETIR2506882 Journal of Emerging Technologies and Innovative Research, vol. 12, 2025, [Online]. Available: https://learn.microsoft.com/en-us/azure/architecture/data-science-process/lifecycle
[4] O. Ilmiah Guru Besar Institut Teknologi Bandung and P. Kridanto Surendro, “FORUM GURU BESAR,” 2024. [Online]. Available: www.itbpress.id
[5] M. Ilham et al., “ANALISA BEBAN PENDINGIN PADA RUANG DATA CENTER DI PT BSH,” Jurnal Teknologi Industri, vol. 12, no. 1, 2023.
[6] “Perpres Nomor 22 Tahun 2017,” 2017.
[7] H. A. Ounifi, A. Gherbi, and N. Kara, “Deep machine learning-based power usage effectiveness prediction for sustainable cloud infrastructures,” Sustainable Energy Technologies and Assessments, vol. 52, Aug. 2022, doi: 10.1016/j.seta.2022.101967.
[8] J. Bi, S. Li, H. Yuan, Z. Zhao, and H. Liu, “Deep Neural Networks for Predicting Task Time Series in Cloud Computing Systems,” 2019, [Online]. Available: https://github.com/google/cluster-data
[9] K. Saranraj and C. M. Liu, “Enhancing Power Efficiency Prediction in Small-Scale Data Centers using Attention-Based Deep Neural Networks,” in 2023 IEEE 5th International Conference on Architecture, Construction, Environment and Hydraulics, ICACEH 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 44–49. doi: 10.1109/ICACEH59552.2023.10452493.
[10] M. Y. Anshori, V. Asy’Ari, T. Herlambang, and I. W. Farid, “Forecasting occupancy rate using neural network at Hotel R,” in 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), 2023, pp. 347–351. doi: 10.1109/ICAMIMIA60881.2023.10427752.
[11] T. Deepika and P. Prakash, “Power consumption prediction in cloud data center using machine learning,” International Journal of Electrical and Computer Engineering, vol. 10, no. 2, pp. 1524–1532, 2020, doi: 10.11591/ijece.v10i2.pp1524-1532.
[12] H. Dhake, Y. Kashyap, and P. Kosmopoulos, “Algorithms for Hyperparameter Tuning of LSTMs for Time Series Forecasting,” Remote Sens (Basel), vol. 15, no. 8, Apr. 2023, doi: 10.3390/rs15082076.
[13] A. W. Saputra, A. P. Wibawa, U. Pujianto, A. B. Putra Utama, and A. Nafalski, “LSTM-based Multivariate Time-Series Analysis: A Case of Journal Visitors Forecasting,” ILKOM Jurnal Ilmiah, vol. 14, no. 1, pp. 57–62, Apr. 2022, doi: 10.33096/ilkom.v14i1.1106.57-62.
[14] M. Hanasov, M. Wolter, and E. Glende, “Time Series Data Splitting for Short-Term Load Forecasting,” 2022.
[15] J. Han, J. Pei, and H. Tong, “Data Mining: Concepts and Techniques,” 2023.
[16] N. Khatun, “Applications of Normality Test in Statistical Analysis,” Open J Stat, vol. 11, no. 01, pp. 113–122, 2021, doi: 10.4236/ojs.2021.111006.
[17] A. Chernikov, C. W. Tan, P. Montero-Manso, and C. Bergmeir, “FRANS: Automatic Feature Extraction for Time Series Forecasting,” Sep. 2022, [Online]. Available: http://arxiv.org/abs/2209.07018
[18] N. P. Nur Fauzi, S. Khomsah, and A. D. Putra Wicaksono, “Penerapan Feature Engineering dan Hyperparameter Tuning untuk Meningkatkan Akurasi Model Random Forest pada Klasifikasi Risiko Kredit,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 12, no. 2, pp. 251–262, Apr. 2025, doi: 10.25126/jtiik.2025128472.
[19] F. Chollet, Deep Learning with Python. 2021.
[20] N. Alifiya, S. Putri, D. E. Ratnawati, and B. Rahayudi, “Prediksi Kebangkrutan Menggunakan Metode Backpropagation (Studi Kasus: Perseroan Terbatas Terdaftar Pada Bursa Efek Indonesia),” vol. 3, no. 3, pp. 2548–964, 2019, [Online]. Available: http://j-ptiik.ub.ac.id
[21] C. Tzagkarakis, P. Charalampidis, S. Roubakis, A. Fragkiadakis, and S. Ioannidis, “Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments,” Sep. 2022, [Online]. Available: http://arxiv.org/abs/2206.07784
REFERENCES
[1] laureguialzo, . Enrique, “PUE: The Green Grid metric for evaluating the energy efficiency in DC (Data Center). Measurement method using the Power Demand.,” 2011.
[2] V. Avelar, S. Electric, D. Azevedo, A. French, and E. Network Power, “PUETM: A COMPREHENSIVE EXAMINATION OF THE METRIC EDITORS,” 2012.
[3] A. Kumar Jain, “Predictive Analytics-Driven Management of Data Center Lifecycles,” JETIR2506882 Journal of Emerging Technologies and Innovative Research, vol. 12, 2025, [Online]. Available: https://learn.microsoft.com/en-us/azure/architecture/data-science-process/lifecycle
[4] O. Ilmiah Guru Besar Institut Teknologi Bandung and P. Kridanto Surendro, “FORUM GURU BESAR,” 2024. [Online]. Available: www.itbpress.id
[5] M. Ilham et al., “ANALISA BEBAN PENDINGIN PADA RUANG DATA CENTER DI PT BSH,” Jurnal Teknologi Industri, vol. 12, no. 1, 2023.
[6] “Perpres Nomor 22 Tahun 2017,” 2017.
[7] H. A. Ounifi, A. Gherbi, and N. Kara, “Deep machine learning-based power usage effectiveness prediction for sustainable cloud infrastructures,” Sustainable Energy Technologies and Assessments, vol. 52, Aug. 2022, doi: 10.1016/j.seta.2022.101967.
[8] J. Bi, S. Li, H. Yuan, Z. Zhao, and H. Liu, “Deep Neural Networks for Predicting Task Time Series in Cloud Computing Systems,” 2019, [Online]. Available: https://github.com/google/cluster-data
[9] K. Saranraj and C. M. Liu, “Enhancing Power Efficiency Prediction in Small-Scale Data Centers using Attention-Based Deep Neural Networks,” in 2023 IEEE 5th International Conference on Architecture, Construction, Environment and Hydraulics, ICACEH 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 44–49. doi: 10.1109/ICACEH59552.2023.10452493.
[10] M. Y. Anshori, V. Asy’Ari, T. Herlambang, and I. W. Farid, “Forecasting occupancy rate using neural network at Hotel R,” in 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), 2023, pp. 347–351. doi: 10.1109/ICAMIMIA60881.2023.10427752.
[11] T. Deepika and P. Prakash, “Power consumption prediction in cloud data center using machine learning,” International Journal of Electrical and Computer Engineering, vol. 10, no. 2, pp. 1524–1532, 2020, doi: 10.11591/ijece.v10i2.pp1524-1532.
[12] H. Dhake, Y. Kashyap, and P. Kosmopoulos, “Algorithms for Hyperparameter Tuning of LSTMs for Time Series Forecasting,” Remote Sens (Basel), vol. 15, no. 8, Apr. 2023, doi: 10.3390/rs15082076.
[13] A. W. Saputra, A. P. Wibawa, U. Pujianto, A. B. Putra Utama, and A. Nafalski, “LSTM-based Multivariate Time-Series Analysis: A Case of Journal Visitors Forecasting,” ILKOM Jurnal Ilmiah, vol. 14, no. 1, pp. 57–62, Apr. 2022, doi: 10.33096/ilkom.v14i1.1106.57-62.
[14] M. Hanasov, M. Wolter, and E. Glende, “Time Series Data Splitting for Short-Term Load Forecasting,” 2022.
[15] J. Han, J. Pei, and H. Tong, “Data Mining: Concepts and Techniques,” 2023.
[16] N. Khatun, “Applications of Normality Test in Statistical Analysis,” Open J Stat, vol. 11, no. 01, pp. 113–122, 2021, doi: 10.4236/ojs.2021.111006.
[17] A. Chernikov, C. W. Tan, P. Montero-Manso, and C. Bergmeir, “FRANS: Automatic Feature Extraction for Time Series Forecasting,” Sep. 2022, [Online]. Available: http://arxiv.org/abs/2209.07018
[18] N. P. Nur Fauzi, S. Khomsah, and A. D. Putra Wicaksono, “Penerapan Feature Engineering dan Hyperparameter Tuning untuk Meningkatkan Akurasi Model Random Forest pada Klasifikasi Risiko Kredit,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 12, no. 2, pp. 251–262, Apr. 2025, doi: 10.25126/jtiik.2025128472.
[19] F. Chollet, Deep Learning with Python. 2021.
[20] N. Alifiya, S. Putri, D. E. Ratnawati, and B. Rahayudi, “Prediksi Kebangkrutan Menggunakan Metode Backpropagation (Studi Kasus: Perseroan Terbatas Terdaftar Pada Bursa Efek Indonesia),” vol. 3, no. 3, pp. 2548–964, 2019, [Online]. Available: http://j-ptiik.ub.ac.id
[21] C. Tzagkarakis, P. Charalampidis, S. Roubakis, A. Fragkiadakis, and S. Ioannidis, “Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments,” Sep. 2022, [Online]. Available: http://arxiv.org/abs/2206.07784
Published
2026-10-23
Section
Articles
Copyright (c) 2026 Teguh Herlambang
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.jpg)
