▷ Learning-based Energy Consumption Prediction

⭐⭐⭐⭐ Learning-based Energy Consumption Prediction

• ➡️ #EnergyConsumptionPrediction #Energy #ESP32 #PZMT004 #OpenSource #OpenHardware #RegressionLearner #SustainableEnergyInformation #DataCenter
• ✅ #NodeRed #EmbeddedSystems #MIcrocontroller #Phyton #MQTT #MySQL #Telegram
• ➡️ #SEIT2022: The 12th International Conference on Sustainable Energy Information Technology. August 9-11, 2022, Niagara Falls, Ontario, Canada
• ✅The Matlab code used for data processing are available in: https://github.com/vasanza/Matlab_Code/tree/EnergyConsumptionPredictionDatacenter
• ✅The dataset used for data processing are available in: https://ieee-dataport.org/open-access/data-server-energy-consumption-dataset
• ⭐ When using this resource, please cite the original publication:
• Estrada, R., Torres, D., Bazurto, A., & Valeriano, I. (2022). Learning-based Energy Consumption Prediction. Procedia Computer Science, 203, 272-279.
• ✅ Abstract:
• As more people send information to the cloud-fog infrastructure, this brings many problems to the management of computer energy consumption. Therefore, energy consumption management of servers, fog devices and cloud computing platform should be investigated to comply with the Green IT requirement. In this paper, we propose an energy consumption prediction model consisting of several components such as hardware design, data pre-processing, characteristics extraction and selection. Our main goal is to develop a non-invasive meter based on a network of sensors that includes a microcontroller, the MQTT communication protocol and the energy measurement module. This meter measures voltage, current, power, frequency, energy and power factor while a dashboard is used to present the energy measurements in real-time. In particular, we perform measurements using a workstation that has similar characteristics to the servers of a Datacenter locate at the Information Technology Center in Guayaquil - Ecuador, which currently provide this type of services in Ecuador. For convenience, we evaluated different linear regression models to select the best one and to predict future energy consumption based on the several measurements from the workstation during several hours which enables the consumer to optimize and to reduce the maintenance costs of the IT equipment. The supervised machine learning algorithms presented in this work allow us to predict the energy consumption by hours and by days.

✅ Video of the talk:

✅ Conference content:

• ➡️ Introduction

• ➡️ Related Work

• ➡️ System Model

• ➡️ Methodology

• ➡️ Results and Discussion

• ➡️ Conclusions

✅ References:

1. El Zoghbi, B., & Chedrawi, C. (2020). Cloud Computing and the New Role of IT Service Providers in Lebanon: A Service Dominant Logic Approach. In ICT for an Inclusive World (pp. 425-437). Springer, Cham.
2. Balaras, C. A., Lelekis, J., Dascalaki, E. G., & Atsidaftis, D. (2017). High performance data centers and energy efficiency potential in Greece. Procedia environmental sciences, 38, 107-114.
3. Menezes, A. C., Cripps, A., Bouchlaghem, D., & Buswell, R. (2012). Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap. Applied energy, 97, 355-364.
4. Avancini, D. B., Rodrigues, J. J., Martins, S. G., Rabˆelo, R. A., Al-Muhtadi, J., & Solic, P. (2019). Energy meters evolution in smart grids: A review. Journal of cleaner production, 217, 702-715.
5. Miglani, A., Kumar, N., Chamola, V., & Zeadally, S. (2020). Blockchain for Internet of Energy management: Review, solutions, and challenges. Computer Communications, 151, 395-418.
6. Acton, M., Bertoldi, P., Booth, J., Newcombe, L., Rouyer, A., & Tozer, R. (2017). 2018 Best Practice Guidelines for the EU Code of Conduct on Data Centre Energy Efficiency. Publications Office of the European Union, Luxembourg, Tech. Report. EUR 29103 EN, 2018.
7. Hu, X., Li, P.,& Sun, Y. (2021). Minimizing Energy Cost for Green Data Center by Exploring Heterogeneous Energy Resource. Journal of Modern Power Systems and Clean Energy, 9(1), 148-159.
8. Akbari, E., Cung, F., Patel, H., Razaque, A., Dalal, H. N., Incorporation of weighted linear prediction technique and M/M/1 Queuing Theory for improving energy efficiency of Cloud computing datacenters, 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT) (pp. 1-5).
9. Lin, W., Wu, G., Wang, X., Li, K. (2019). An artificial neural network approach to power consumption model construction for servers in cloud data centers. IEEE Transactions on Sustainable Computing, 5(3), 329-340.
10. Merizig, A., Bendahmane, T., Merzoug, S., & Kazar, O. (2020, February). Machine Learning Approach for Energy Consumption Prediction in Datacenters. 2nd International Conference on Mathematics and Information Technology (pp. 142-148).
11. Adrian Bazurto, Danny Torres, Víctor Asanza, Rebeca Estrada. (2021). Data Server Energy Consumption Dataset. IEEE Dataport. https://dx.doi.org/10.21227/x6jw-m015
12. Y. Guo, W. Wang and X. Wang, "A Robust Linear Regression Feature Selection Method for Data Sets with Unknown Noise," in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2021.3076891.
13. V. Asanza, R. Estrada, J. Miranda, L. Rivas and D. Torres, Performance Comparison of Database Server based on SoC FPGA and ARM Processor,2021 IEEE Latin-American Conference on Communications, pp. 1-6.
14. V. Asanza, R. E. Pico, D. Torres, S. Santillan and J. Cadena, FPGA Based Meteorological Monitoring Station, 2021 IEEE Sensors Applications Symposium (SAS), 2021, pp. 1-6, doi: 10.1109/SAS51076.2021.9530151.
15. V. A. Armijos, N. S. Chan, R. Saquicela and L. M. Lopez, Monitoring of system memory usage embedded in FPGA, 2020 International Conference on Applied Electronics (AE), 2020, pp. 1-4.
16. Asanza V., Sanchez G., Cajo R., Peláez E. (2021) Behavioral Signal Processing with Machine Learning Based on FPGA. In: Botto-Tobar M., Zamora W., Larrea Pl´ua J., Bazurto Roldan J., Santamar´ıa Philco A. (eds) Systems and Information Sciences. ICCIS 2020. Advances in Intelligent Systems and Computing, vol 1273. Springer, Cham.

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