Evaluation and optimization of renewable energy in the Quartier Nord

The energy demand increases worldwide and due to the fact that fossil fuel is still dominating the energy mix, the corresponding CO2 emission becomes a more serious problem that threatens the global environment. The building sector is the largest energy consumer and it is of vital significance to reduce the primary energy consumption thus the CO2 emission in this sector. Under this constraint, the standards were set up to design buildings with relatively low energy consumption. Following the standard, the proposal phase of the buildings can be well regulated. However, it is equally important to check the energy consumption of the building in operating condition. In this project, a specific building was selected and its energy consumption condition was simulated both at building and neighbourhood level respectively. EnergyPlus was chosen to run the simulation at single building level and the results were compared with Minergie standard and the monitored data for the building. Another software, CitySim working at a larger scale, is also used. In order to validate the accuracy of the simulations, a benchmark was done based on the calculations of the idea load of the building in EnergyPlus and CitySim. The sources of errors were analysed in detail. At last, renewable resources were also integrated into the building to explore its plotentialof being a net-zero energy building.

Assessing the gap between a normative and a reality-based model of building LCA

Stefano Cozza, Endrit Hoxha, Thomas Bernard Paul Jusselme, Didier Albert Simon Vuarnoz

Recognized as a powerful methodology for the evaluation of environmental burdens, life cycle assessment (LCA) must be performed with close-to-reality inputs to be robust and accurate. However, the necessary real-world data is hardly available at the design stage, resulting in current LCA practice being mainly based on standards and norms as hypothesis of building contexts, therefore inducing uncertainties in results. The current paper presents a methodology to collect a subset of such input data in a function of the context more accurately than the standards. It then studies the impact of such uncertainties in the LCA results. Through an academic building case study that measured data concerning the building occupancy (A), i.e. the hourly occupancy rate and density, the hourly appliance consumption rate (B), and the hourly conversion factors of environmental impact of the electricity mix (C), the LCA results for the GHG emissions and primary energy consumption are compared between normative- and measurements-based input parameters. The measured occupancy rate (A) is shown to impact the LCA results the most, especially the embedded impacts, by implying a new occupancy density: the building population increase of +32% leads to a significant increase of the embedded impacts related to furniture. The variability in appliance usage (B) is marginal between measures and standards and therefore does not lead to a significant change in LCA results. The use of hourly conversion factors (C) indicates an underestimation of the GHG emissions and, at the same time, an overestimation of the primary energy when assessed with mean annual values. The combined effect of simultaneously using the three reality-based input parameters (A, B, C) mostly affects the non-renewable part of the cumulative energy demand indicator (-9% reduction of the operational part), followed by the cumulative energy demand (-7%) and GHG emissions indicators (-3%). The research findings affect not only LCA research but also practitioners such as architects or building contractors who need to respect ambitious environmental targets.

2020

Reinforcement Learning for proactive operation of residential energy systems by learning stochastic occupant behavior and fluctuating solar energy: Balancing comfort, hygiene and energy use

Amirreza Heidari, Dolaana Khovalyg, François Maréchal

When it comes to residential buildings, there are several stochastic parameters, such as renewable energy production, outdoor air conditions, and occupants’ behavior, that are hard to model and predict accurately, with some being unique in each specific building. This increases the complexity of developing a generalizable optimal control method that can be transferred to different buildings. Rather than hard-programming human knowledge into the controller (in terms of rules or models), a learning ability can be provided to the controller such that over the time it can learn by itself how to maintain an optimal operation in each specific building. This research proposes a model-free control framework based on Reinforcement Learning that takes into account the stochastic hot water use behavior of occupants, solar power generation, and weather conditions, and learns how to make a balance between the energy use, occupant comfort and water hygiene in a solar-assisted space heating and hot water production system. A stochastic-based offline training procedure is proposed to give a prior experience to the agent in a safe simulation environment, and further ensure occupants comfort and health when the algorithm starts online learning on the real house. To make a realistic assessment without interrupting the occupants, weather conditions and hot water use behavior are experimentally monitored in three case studies in different regions of Switzerland, and the collected data are used in simulations to evaluate the proposed control framework against two rule-based methods. Results indicate that the proposed framework could achieve an energy saving from 7% to 60%, mainly by adapting to solar power generation, without violating comfort or compromising the health of occupants.

2022

Assessing the gap between a normative and a reality-based model of building LCA

Stefano Cozza, Endrit Hoxha, Thomas Bernard Paul Jusselme, Didier Albert Simon Vuarnoz

2020

Reinforcement Learning for proactive operation of residential energy systems by learning stochastic occupant behavior and fluctuating solar energy: Balancing comfort, hygiene and energy use

Amirreza Heidari, Dolaana Khovalyg, François Maréchal

2022