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SONDER: A Data-Driven Methodology for Designing Net-Zero Energy Public Buildings

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Big Data Analytics and Knowledge Discovery (DaWaK 2020)

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The reduction of carbon emissions into the atmosphere has become an urgent health issue. The energy in buildings and their construction represents more than 1/3 of final global energy consumption and contributes to nearly 1/4 of greenhouse gas emissions worldwide. Heating, Ventilation, and Air-Conditioning (HVAC) systems are major energy consumers and responsible for about 18% of all building energy use. To reduce this huge amount of energy, the Net-Zero Energy Building (nZEB) concept has been imposed by energy authorities. They recommend a massive use of renewable energy technology. With the popularization of Smart Grid, Internet of Things devices, and the Machine Learning (ML), a couple of data-driven approaches emerged to reach this crucial objective. By analysing these approaches, we figure out that they lack a comprehensive methodology with a well-identified life cycle that favours collaboration between nZEB actors. In this paper, we share our vision for developing Energy Management Systems for nZEB as part of IMPROVEMENT EU Interreg Sudoe programm. First, we propose a comprehensive methodology (SONDER), associated with a well-identified life cycle for developing data-driven solutions. Secondly, an instantiation of this methodology is given by considering a case study for predicting the energy consumption of the domestic hot water system in the Regional Hospital of La Axarquia, Spain that includes gas and electricity sections. This prediction is conducted using four ML techniques: multivariate regression, XGBoost, Random Forest and ANN. Our obtained results show the effectiveness of SONDER by offering a fluid collaboration among project actors and the prediction efficiency of ANN.

L. Bellatreche—This work has been carried out with the financial support of the European Regional Development Fund (ERDF) under the program Interreg SUDOE SOE3/P3/E0901 (Project IMPROVEMENT).

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  1. Amasyali, K., El-Gohary, N.M.: A review of data-driven building energy consumption prediction studies. Renew. Sustain. Energy Rev. 81, 1192–1205 (2018)

    Article  Google Scholar 

  2. Bachman, C.W.: Summary of current work - ansi/x3/sparc/study group - database systems. FDT - Bull. ACM SIGMOD 6(3), 16–39 (1974)

    Article  Google Scholar 

  3. Bordons, C., Garcia-Torres, F., Ridao, M.A.: Model Predictive Control of Microgrids. AIC. Springer, Cham (2020).

    Book  Google Scholar 

  4. Casconea, Y., Luigi, M.F., GianlucaSerale, G.: Ethical issues of monitoring sensor networks for energy efficiency in smart buildings: a case study. Energy Procedia 134, 337–345 (2017)

    Article  Google Scholar 

  5. Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: ACM SIGKDD, pp. 785–794 (2016)

    Google Scholar 

  6. El-hawary, M.E.: The smart grid: state-of-the-art and future trends. Electric Power Comp. Syst. 42(3–4), 239–250 (2014)

    Article  Google Scholar 

  7. Garcia-Torres, F., Bordons, L.V.C.: Optimal load sharing of hydrogen-based microgrids with hybrid storage using model predictive control. IEEE TIE 63(8), 4919–4928 (2016)

    Google Scholar 

  8. Fankam, C., Jean, S., Bellatreche, L., Ameur, Y.A.: Extending the ANSI/SPARC architecture database with explicit data semantics: an ontology-based approach. In: ECSA, pp. 318–321 (2008)

    Google Scholar 

  9. García, S., Luengo, J., Herrera, F.: Tutorial on practical tips of the most influential data preprocessing algorithms in data mining. Knowl. Based Syst. 98, 1–29 (2016)

    Article  Google Scholar 

  10. Garcia-Torres, F., Bordons, C.: Optimal economical schedule of hydrogen-based microgrids with hybrid storage using model predictive control. IEEE TIE 62(8), 5195–5207 (2015)

    Google Scholar 

  11. Golfarelli, M.: Data warehouse life cycle and design. In: Encyclopedia of Database Systems, Second Edition (2018)

    Google Scholar 

  12. Khouri, S., Semassel, K., Bellatreche, L.: Managing data warehouse traceability: a life-cycle driven approach. In: CAiSE, pp. 199–213 (2015)

    Google Scholar 

  13. Lanasri, D., Ordonez, C., Bellatreche, L., Khouri, S.: ER4ML: an ER modeling tool to represent data transformations in data science. In: ER Demos, pp. 123–127 (2019)

    Google Scholar 

  14. Liu, Z., Wu, D., Wei, H., Cao, G.: Machine learning for building energy and indoor environment: a perspective. ArXiv abs/1801.00779 (2018)

    Google Scholar 

  15. Meehan, J., Aslantas, C., Zdonik, S., Tatbul, N., Du, J.: Data ingestion for the connected world. In: CIDR (2017)

    Google Scholar 

  16. Saleh, M., Esa, Y., Mohamed, A.A.: Communication-based control for DC microgrids. IEEE Trans. Smart Grid 10(2), 2180–2195 (2019)

    Article  Google Scholar 

  17. Sartori, I., Marszal, A., Napolitano, A., Pless, S., Torcellini, P., Voss, K.: Criteria for Definition of Net Zero Energy Buildings, pp. 25–36 (2010)

    Google Scholar 

  18. Wissner, M.: The smart grid - a saucerful of secrets? Appl. Energy 88(7), 2509–2518 (2011)

    Article  Google Scholar 

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Correspondence to Ladjel Bellatreche .

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Bellatreche, L., Garcia, F., Pham, D.N., Jiménez, P.Q. (2020). SONDER: A Data-Driven Methodology for Designing Net-Zero Energy Public Buildings. In: Song, M., Song, IY., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2020. Lecture Notes in Computer Science(), vol 12393. Springer, Cham.

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  • Print ISBN: 978-3-030-59064-2

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