The DTS Comfie module also calculates the energy consumption of the equipment (Dynamic Energy Simulation or DES) at each time step with the possibility of recovering heat losses, evaluates several comfort indicators and has a utility to manipulate and generate weather data files.

Comfie is linked to Amapola, which makes it possible to identify the least expensive solutions, anticipate uses and optimise the reliability of forecasts. It is therefore possible to assess energy consumption within the framework of the energy efficiency guarantee with a risk of an overrun of less than 5%. Thanks to data from smart sensors, the software program takes occupants and their behaviour into account.

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The general principle of Eurovia’s PowerRoad^® system coupled to a building is to collect free heat from the sun thanks to a heat exchanger integrated into the road surface. This energy is then used directly via a heat pump or stored in short-term (balloon) and inter-seasonal storage devices, such as vertical geothermal probes (VGP).

On a fixed-term contract at MINES ParisTech under the supervision of Bruno Peuportier, Lucas Striegel is responsible for linking the simulation model of the PowerRoad^® positive-energy road with building models, heat pump and VGP from the Pleiades DES Comfie tool. This chaining is necessary since the functioning of the positive-energy road is intimately linked to that of the heat pump, vertical geothermal probes and, of course, the building because of the power demand and the temperature levels which change over time.

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Heat exchange, air movements, collection, storage and distribution of solar energy, production of electricity, heat and cold are determining variables for the energy efficiency of a building. The modelling tools developed within the Centre for Energy Efficiency of Systems (CES) at MINES ParisTech are used to simulate and anticipate the behaviour of buildings and so identify the heating and cooling needs, the level of thermal comfort or take into account the contribution of local energy production systems such as photovoltaic modules.

The development of a stochastic model of building occupancy made it possible to assess and take into account the behaviours and uses of occupants and visitors to buildings, which represent a key factor in energy consumption during the use phase.

The building energy simulation is supplemented by uncertainty calculations, which make it possible to propose a performance guarantee process including the determination of a level corresponding to a controlled risk. The actual performance level is then verified by measuring and adjusting certain parameters related to the climate and occupants. Thanks to the popularisation of smart sensors and machine learning techniques, it is now possible to take better account of these variables.

At the neighbourhood level, modelling of microclimates makes it possible to simulate the implementation of an urban project in order to estimate the consequences in terms of temperatures (the heat island effect, for example) and air movement.

Optimisation techniques are implemented to develop energy management strategies taking into account the interactions between buildings and networks, in conjunction with the CAS (Automatic Control and Systems Centre). The objective is, for example, to reduce peak demand in buildings, which will therefore better integrate into a network increasingly supplied by renewable but intermittent sources and so facilitate the energy transition. Macroeconomic models have therefore been used to study prospective scenarios concerning the long-term developments in the electricity system, the environmental impacts being strongly influenced by this parameter. Optimisation is also implemented during the design phase in order to identify high-performance solutions at lower cost through the use of genetic algorithms.

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