IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v245y2022ics0360544222001694.html
   My bibliography  Save this article

A machine learning approach based on neural networks for energy diagnosis of telecommunication sites

Author

Listed:
  • Nastro, Francesco
  • Sorrentino, Marco
  • Trifirò, Alena

Abstract

Multi-layer-perceptron-feedforward neural-networks (MLPFFNNs) are proposed to monitor electricity consumption of generic telecommunication sites, thus addressing well-known energy saving and environmental issues. The proposed prediction tool helps infer telecommunication site efficiency, thus enabling undertaking suitable energy intelligence actions (monitoring, control and diagnosis, supervision) as a function of a large number of data and information. A trial-and-error analysis was performed to find the most accurate neural network for such a prediction task. Physical knowledge and intrinsic behavioral features of telecommunication sites indicated that 2 electricity consumptions- and 4 weather-related variables shall populate the input layer. The number of hidden neurons, along with training and test splitting percentages, were selected as the variables to refine within the networks development task. Statistical assessment of accuracy was conducted via joint evaluation of root mean square error and coefficient of determination. The precision and generalization of the best MLPFFNN were successfully assessed on a relevant sites dataset. Prediction percentage errors set to 5.65% and 5.77% for monthly and weekly assessment, respectively, thus confirming the suitability of the proposed modelling approach to meet regulatory standards (i.e., ISO 50001), requiring reliable and continuous monitoring and diagnosis of large fleets of industrial buildings, such as central offices and datacenters.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:245:y:2022:i:c:s0360544222001694
    DOI: 10.1016/j.energy.2022.123266
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222001694
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123266?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Islam, M.S. & Das, Barun K. & Das, Pronob & Rahaman, Md Habibur, 2021. "Techno-economic optimization of a zero emission energy system for a coastal community in Newfoundland, Canada," Energy, Elsevier, vol. 220(C).
    2. 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.
    3. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
    4. 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.
    5. Das, Pronob & Das, Barun K. & Rahman, Mushfiqur & Hassan, Rakibul, 2022. "Evaluating the prospect of utilizing excess energy and creating employments from a hybrid energy system meeting electricity and freshwater demands using multi-objective evolutionary algorithms," Energy, Elsevier, vol. 238(PB).
    6. 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.
    7. Chaudhuri, Tanaya & Soh, Yeng Chai & Li, Hua & Xie, Lihua, 2019. "A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings," Applied Energy, Elsevier, vol. 248(C), pages 44-53.
    8. Bermeo-Ayerbe, Miguel Angel & Ocampo-Martinez, Carlos & Diaz-Rozo, Javier, 2022. "Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems," Energy, Elsevier, vol. 238(PB).
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Vaziri Rad, Mohammad Amin & Kasaeian, Alibakhsh & Niu, Xiaofeng & Zhang, Kai & Mahian, Omid, 2023. "Excess electricity problem in off-grid hybrid renewable energy systems: A comprehensive review from challenges to prevalent solutions," Renewable Energy, Elsevier, vol. 212(C), pages 538-560.
    3. 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).
    4. Zhang, Sheng & Cheng, Yong & Oladokun, Majeed Olaide & Huan, Chao & Lin, Zhang, 2019. "Heat removal efficiency of stratum ventilation for air-side modulation," Applied Energy, Elsevier, vol. 238(C), pages 1237-1249.
    5. Liu, Xiaoqi & Lee, Seungjae & Bilionis, Ilias & Karava, Panagiota & Joe, Jaewan & Sadeghi, Seyed Amir, 2021. "A user-interactive system for smart thermal environment control in office buildings," Applied Energy, Elsevier, vol. 298(C).
    6. Juana Isabel Méndez & Adán Medina & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2022. "Evolving Gamified Smart Communities in Mexico to Save Energy in Communities through Intelligent Interfaces," Energies, MDPI, vol. 15(15), pages 1-29, July.
    7. Manal Ayyad Dhif Alshammry & Saqib Muneer, 2023. "The influence of economic development, capital formation, and internet use on environmental degradation in Saudi Arabia," Future Business Journal, Springer, vol. 9(1), pages 1-16, December.
    8. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    9. Zhang, Weiping & Maleki, Akbar, 2022. "Modeling and optimization of a stand-alone desalination plant powered by solar/wind energies based on back-up systems using a hybrid algorithm," Energy, Elsevier, vol. 254(PC).
    10. Xia, Guanghui & Zhuang, Dawei & Ding, Guoliang & Lu, Jingchao, 2020. "A quasi-three-dimensional distributed parameter model of micro-channel separated heat pipe applied for cooling telecommunication cabinets," Applied Energy, Elsevier, vol. 276(C).
    11. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    12. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    13. Das, Pronob & Das, Barun K. & Rahman, Mushfiqur & Hassan, Rakibul, 2022. "Evaluating the prospect of utilizing excess energy and creating employments from a hybrid energy system meeting electricity and freshwater demands using multi-objective evolutionary algorithms," Energy, Elsevier, vol. 238(PB).
    14. Amir Faraji & Maria Rashidi & Fatemeh Rezaei & Payam Rahnamayiezekavat, 2023. "A Meta-Synthesis Review of Occupant Comfort Assessment in Buildings (2002–2022)," Sustainability, MDPI, vol. 15(5), pages 1-36, February.
    15. Giacomo Segala & Roberto Doriguzzi-Corin & Claudio Peroni & Matteo Gerola & Domenico Siracusa, 2023. "EECO: An AI-Based Algorithm for Energy-Efficient Comfort Optimisation," Energies, MDPI, vol. 16(21), pages 1-28, October.
    16. Ham, Sang-Woo & Kim, Min-Hwi & Choi, Byung-Nam & Jeong, Jae-Weon, 2015. "Energy saving potential of various air-side economizers in a modular data center," Applied Energy, Elsevier, vol. 138(C), pages 258-275.
    17. Costa Climent, Ricardo & Haftor, Darek M., 2021. "Business model theory-based prediction of digital technology use: An empirical assessment," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    18. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost," Energy, Elsevier, vol. 192(C).
    19. Chen, Min & Gao, Ciwei & Song, Meng & Chen, Songsong & Li, Dezhi & Liu, Qiang, 2020. "Internet data centers participating in demand response: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    20. Mao, Ning & Hao, Jingyu & He, Tianbiao & Song, Mengjie & Xu, Yingjie & Deng, Shiming, 2019. "PMV-based dynamic optimization of energy consumption for a residential task/ambient air conditioning system in different climate zones," Renewable Energy, Elsevier, vol. 142(C), pages 41-54.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:245:y:2022:i:c:s0360544222001694. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.