Partner: ENSMA and CNH2

Authors: Ladjel Bellatreche, Felix Garcia, Don Nguyen Pham, Pedro Quintero Jiménez

Publicacion access here.

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.

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