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 energy simulation of buildings makes it possible to evluate their forecast energy consumption and level of thermal comfort, on the basis of assumptions made on uses and the climate. But the real results will depend on the actual behaviour of the occupants and actual climatic variations. So that eco-design is not limited to offering a product, but helps to ensure a genuine level of service, possibly with a notion of energy efficiency guarantee, smart sensors can enhance the simulation by providing data on these aspects of behaviour and of climate.

Objectives

The aim of the PhD thesis is to take advantage of smart sensors to better anticipate occupant behaviour and improve the reliability of energy simulation.
A first part of the thesis aims to improve, thanks to the collection of information from smart sensors (temperature, humidity, noise, energy consumption, CO₂concentration), statistical models relating to the use of premises and the behaviour of occupants.
A second part concerns improving the reliability of the energy simulation of buildings. The aim is to develop new model calibration methods taking advantage of sensors.
The enhanced simulation will be tested on a case study, in particular, to find out which procedures are the most relevant in an energy efficiency guarantee process. The calculation modules developed as part of the thesis will be sent to a software publisher in order to study their distribution.

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The energy efficiency of the building is based on solutions such as constructing compact housing, favouring solar gain and natural lighting, using insulating materials allowing air-tightness or heat storage, using double flow ventilation systems which recover and distribute heating, alternative energy sources (solar, geothermal, photovoltaic) and automatic consumption regulation devices. However, low-energy buildings often consume more than expected. According to a CEREMA study conducted between 2012 and 2016, 50% of efficient buildings consume a surplus of 10 KWhep per sq.m per year. In 25% of cases, this excess is 35 kWhep per sq.m per year. According to the study, the differences observed depend in part on user behaviour. As buildings become efficient, these deviations become increasingly significant compared to the overall performance of the project. They can easily reach 20 to 30% of the total consumption.

This discrepancy, due to an imprecise estimate of actual uses, was also observed by Eric Vorger, former doctoral student at MINES ParisTech and co-founder of Kocliko, a start-up that offers digital tools to optimise and guarantee the energy efficiency of buildings. “Human behaviour is simplistically modelled in building energy simulation software. However, its impact is considerable and it is the source of significant variations between simulation results and in situ measurements. Short-term predictions (of the order of 24 hours), used to regulate energy consumption in real-time, as well as medium-term predictions (one year), useful for sizing renovation operations, are marred by very significant uncertainties (often greater than 50%) because of this lack of knowledge of uses”.

The arrival of algorithms in eco-design makes it possible to analyse, understand, study and forecast user practices better and better and take them into account to optimise energy efficiency. As part of his PhD project, Eric Vorger, modelled the energy consumption of buildings resulting from the presence and activities of inhabitants. This occupancy model is coupled with the dynamic thermal simulation tool Pléiades+COMFIE, developed by MINES ParisTech with the support of the lab recherche environnement.
The presence of people in a home and their activities are valuable information that makes it possible to predict the opening or closing of windows and blinds, the use of lighting and domestic equipment, the occupation of certain rooms in the home, the number of people in a room and the metabolism of the occupants which influence the ambient temperature.

Eric has developed probabilistic models based on INSEE data (e.g. the “Time Use” surveys) and measurement campaigns. Thanks to these models, the same home can be simulated 10,000 times with, each time, occupants having different socio-demographic characteristics which determine different schedules. The second graph illustrates the average daily scenario of a virtual individual, produced from a series of simulations. Other simulations were carried out successively with the same dwelling and different types of household: a large family, a working couple, a retired person, etc. By taking into account the composition of households, more reliable forecasts of energy consumption are obtained, which allows contractors to commit to realistic energy efficiency targets and achieve them.

This design support tool enables design to be optimised thanks to realistic usage forecasts. There are therefore fewer design errors, and consumption and comfort forecasts are more reliable.  Kocliko has also developed solutions for energy management during the operational phase. Based on the observation that connected objects are now technology that is accessible at low cost, the start-up proposes to exploit the “Internet of things” in order to extend the collection of measurements in a housing unit: temperature, humidity percentage, electricity consumption, thermostat usage, etc. The machine learning algorithms developed by Kocliko combine this data to reconstruct actual uses and manage energy consumption. An energy saving of at least 20% can thus be achieved during the operating phase.

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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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