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An innovative energy efficiency metric for data analytics and diagnostics in telecommunication applications

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  • Sorrentino, Marco
  • Bruno, Marco
  • Trifirò, Alena
  • Rizzo, Gianfranco

Abstract

This paper introduces and indicates how to deploy a novel energy metric, to be adopted for advanced monitoring and diagnosis of telecommunication central offices and data centers. Such an activity is motivated by the worldwide increasing telecommunication players awareness of the need to substantially reduce their energy demand, both to increase their market competitiveness and meet the stringent greenhouse gas emission regulations. The proposed metric, named utilization factor, was thus defined according to the peculiar energy breakdown of central offices. The aim was to conceive an index that focuses more on telecommunication energy adsorption and, in turn, enables climatic independent efficiency evaluation of the central offices under investigation. Then, suitable data-processing techniques were applied to develop a reliable utilization factor predictive model, whose identification and validation tasks were carried-out over an extended central offices database. The availability of a large amount of data was suitably exploited through data analytics approaches, particularly enabling diagnosis-oriented model development. Upon successful testing of its accuracy, the model was finally proven to be a strategic tool to perform model-based fault detection and isolation of relevant faults that may arise during central office monitoring tasks, such as abnormal data acquisition and non-optimal energy management. The suitability of the proposed metric, to be deployed as an innovative and synthetic energy index, was evaluated over an extended fleet of central offices and data-centers. It was found that the majority (about 70%) of tested central offices exhibits either adequate energy and thermal management or sensor-related only faults.

Suggested Citation

  • Sorrentino, Marco & Bruno, Marco & Trifirò, Alena & Rizzo, Gianfranco, 2019. "An innovative energy efficiency metric for data analytics and diagnostics in telecommunication applications," Applied Energy, Elsevier, vol. 242(C), pages 1539-1548.
    • Handle: RePEc:eee:appene:v:242:y:2019:i:c:p:1539-1548
    DOI: 10.1016/j.apenergy.2019.03.173
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    1. Changfeng Yuan & Hui Cui & Bin Tao & Siming Ma, 2018. "Cause factors in emergency process of fire accident for oil–gas storage and transportation based on fault tree analysis and modified Bayesian network model," Energy & Environment, , vol. 29(5), pages 802-821, August.
    2. Xin Lu & David J. Wrathall & Pål Roe Sundsøy & Md. Nadiruzzaman & Erik Wetter & Asif Iqbal & Taimur Qureshi & Andrew J. Tatem & Geoffrey S. Canright & Kenth Engø-Monsen & Linus Bengtsson, 2016. "Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen," Climatic Change, Springer, vol. 138(3), pages 505-519, October.
    3. Hong, Tianzhen & Yang, Le & Hill, David & Feng, Wei, 2014. "Data and analytics to inform energy retrofit of high performance buildings," Applied Energy, Elsevier, vol. 126(C), pages 90-106.
    4. Jouin, Marine & Gouriveau, Rafael & Hissel, Daniel & Péra, Marie-Cécile & Zerhouni, Noureddine, 2016. "Degradations analysis and aging modeling for health assessment and prognostics of PEMFC," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 78-95.
    5. Polverino, Pierpaolo & Sorrentino, Marco & Pianese, Cesare, 2017. "A model-based diagnostic technique to enhance faults isolability in Solid Oxide Fuel Cell systems," Applied Energy, Elsevier, vol. 204(C), pages 1198-1214.
    6. Garimella, Suresh V. & Persoons, Tim & Weibel, Justin & Yeh, Lian-Tuu, 2013. "Technological drivers in data centers and telecom systems: Multiscale thermal, electrical, and energy management," Applied Energy, Elsevier, vol. 107(C), pages 66-80.
    7. Li, Han & You, Shijun & Zhang, Huan & Zheng, Wandong & Zheng, Xuejing & Jia, Jie & Ye, Tianzhen & Zou, Lanjun, 2017. "Modelling of AQI related to building space heating energy demand based on big data analytics," Applied Energy, Elsevier, vol. 203(C), pages 57-71.
    8. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    9. Feng, Jing-Chun & Yan, Jinyue & Yu, Zhi & Zeng, Xuelan & Xu, Weijia, 2018. "Case study of an industrial park toward zero carbon emission," Applied Energy, Elsevier, vol. 209(C), pages 65-78.
    10. Chen, Xi & Yang, Hongxing, 2018. "Integrated energy performance optimization of a passively designed high-rise residential building in different climatic zones of China," Applied Energy, Elsevier, vol. 215(C), pages 145-158.
    11. Fan, Cheng & Sun, Yongjun & Shan, Kui & Xiao, Fu & Wang, Jiayuan, 2018. "Discovering gradual patterns in building operations for improving building energy efficiency," Applied Energy, Elsevier, vol. 224(C), pages 116-123.
    12. Zhu, Kai & Cui, Zhuo & Wang, Yabo & Li, Hailong & Zhang, Xiaojing & Franke, Carsten, 2017. "Estimating the maximum energy-saving potential based on IT load and IT load shifting," Energy, Elsevier, vol. 138(C), pages 902-909.
    13. Yao-Liang Chung, 2016. "A Novel Power-Saving Transmission Scheme for Multiple-Component-Carrier Cellular Systems," Energies, MDPI, vol. 9(4), pages 1-18, April.
    14. Benedetti, Miriam & Bonfa', Francesca & Bertini, Ilaria & Introna, Vito & Ubertini, Stefano, 2018. "Explorative study on Compressed Air Systems’ energy efficiency in production and use: First steps towards the creation of a benchmarking system for large and energy-intensive industrial firms," Applied Energy, Elsevier, vol. 227(C), pages 436-448.
    15. Kavadias, K.A. & Apostolou, D. & Kaldellis, J.K., 2018. "Modelling and optimisation of a hydrogen-based energy storage system in an autonomous electrical network," Applied Energy, Elsevier, vol. 227(C), pages 574-586.
    16. Yang, Tian-Jian & Zhang, Yue-Jun & Tang, Su & Zhang, Jing, 2016. "How to assess and manage energy performance of numerous telecommunication base stations: Evidence in China," Applied Energy, Elsevier, vol. 164(C), pages 436-445.
    17. Kuriyama, Akihisa & Abe, Naoya, 2018. "Ex-post assessment of the Kyoto Protocol – quantification of CO2 mitigation impact in both Annex B and non-Annex B countries-," Applied Energy, Elsevier, vol. 220(C), pages 286-295.
    18. Sorrentino, Marco & Acconcia, Matteo & Panagrosso, Davide & Trifirò, Alena, 2016. "Model-based energy monitoring and diagnosis of telecommunication cooling systems," Energy, Elsevier, vol. 116(P1), pages 761-772.
    19. Fan, Cheng & Wang, Jiayuan & Gang, Wenjie & Li, Shenghan, 2019. "Assessment of deep recurrent neural network-based strategies for short-term building energy predictions," Applied Energy, Elsevier, vol. 236(C), pages 700-710.
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    Cited by:

    1. Hou, Juan & Li, Haoran & Nord, Natasa, 2022. "Nonlinear model predictive control for the space heating system of a university building in Norway," Energy, Elsevier, vol. 253(C).
    2. Nastro, Francesco & Sorrentino, Marco & Trifirò, Alena, 2022. "A machine learning approach based on neural networks for energy diagnosis of telecommunication sites," Energy, Elsevier, vol. 245(C).
    3. Qiongzhi Liu & Yifeng Xia, 2023. "The Energy-Saving Effect of Tax Rebates: The Impact of Tax Refunds on Corporate Total Factor Energy Productivity," Energies, MDPI, vol. 16(23), pages 1-19, November.

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