Deep learning optimal control for a solar-biomass system for residential buildings

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Zsembinszki, GabrielZsembinszki, Gabriel - ORCID ID
Fernàndez Camon, CésarFernàndez Camon, César - ORCID ID
Borri, EmilianoBorri, Emiliano - ORCID ID
Cabeza, Luisa F.Cabeza, Luisa F. - ORCID ID
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Nowadays, it is well known that the reduction of energy consumption in buildings is crucial to achieve a substantial reduction of gas emission to the atmosphere and decrease the fast depletion of energy sources. Indeed, buildings are responsible for almost 40% of the overall energy consumption and gas emission into the atmosphere [1], therefore, immediate actions are needed. The reduction of the energy demand through passive solutions (i.e. building envelope) has been taking into account in directives such as the Energy Performance of Buildings Directive [2]. However, the generation of energy through efficient systems and the use of renewable sources is also required to achieve deep decarbonisation of the grid. However, the main disadvantage of renewable sources is represented by their daily and seasonal intermittency. Therefore, a system that allows the combination of more of them can provide a higher flexibility and increase the total share of renewables to meet energy needs in buildings.
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XII National and III International Conference on Engineering Thermodynamics. 29 June – 1 July 2022, Madrid, Spain