The study combines dynamic modelling, photovoltaic simulation and learning optimisation to arbitrate between billing, profitability and avoided emissions.
Published in Scientific Reports, this study proposes a framework for technical, economic and environmental optimization for the renovation of buildings towards the "nearly zero-energy" (nZEB) standard, applied to the real case of a university campus. The approach articulates three bricks: dynamic energy modeling of the building, simulation of photovoltaic installations, and optimization based on machine learning to explore the space of combinations of measurements.
The methodological interest lies in the fact that the trade-off is not carried out on a single criterion. The authors jointly monitor the levelized cost of electricity (LCOE), the return on investment, the internal rate of return, the intensity of energy use and the cumulative CO₂ savings, over a 25-year horizon. The model also incorporates two often neglected effects: the gradual decarbonization of the electricity mix and the dynamic evolution of tariffs.
The identified renovation trajectories achieve a substantial reduction in emissions and a contribution from renewable energy exceeding the 50% threshold required by the nZEB definition, while remaining financially sustainable. The framework aims to give asset managers a decision-making tool rather than a one-size-fits-all recipe.
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