Evaluation of stability and robustness of poly-Si resistors with different dopant concentrations

The stability and robustness of lightly and highly doped poly-Si resistors were evaluated. These resistors exhibited distinct electrical resistance properties and temperature dependences, which can be explained through the grain and grain boundary conduction mechanisms. The resistance shift saturated under the low current stress condition, but continued to increase under the high current stress condition. A novel carrier trapping density model was proposed to explain this behavior. A generalized free energy model that considered stress temperature and stress current dependences was proposed to account for the stability lifetime of a poly-Si resistor based on the resistance shift criterion. Robustness evaluation with transmission line pulse test revealed that the breakdown current exhibited a pulse width dependence which was further explained by a thermal- conduction energy model.

Statistical study of thermoradiative and photovoltaic cells based on a two-level model

We use a two-level energy model to understand the conversion process that takes place in thermoradiative cells and to compare it with the conversion process that happens in photovoltaic cells. In this way, we show that in both kinds of converters the conversion process can be studied as the succession of a change in the populations of the levels that occur at constant chemical potential and a change in the value of the chemical potential of the two levels that happens while keeping their populations constant. As an application of the model, we will discuss why in thermoradiative cells the open-circuit voltage is negative while it is positive in photovoltaic cells. We also show that the expression for the open-circuit voltage is the same in both kinds of cells but that due to the values of the temperatures it is negative in thermoradiative cells and positive in photovoltaic ones.

Energy Sustainability of a Cluster of Buildings with the Application of Smart Grids and Decentralization of Renewable Energy Sources

The article deals with the current state of energy consumption and CO2 emissions in the urban area of building clusters. There are many requirements, such as those set by the EU (FIT 55, decarbonisation in 2050, full electromobility in 2035, or mandatory annual reductions in energy consumption and CO2 production) or societal requirements, such as stable energy supply and its optimisation while significantly reducing CO2 emissions. This inspired us to design an energy model of a building (EMB) with smart grid implementation in a decentralized sustainable energy system. Simulation model studies were carried out to show the possibility of their application in the process of fully satisfying energy needs in terms of reducing their environmental impact. A decentralized photovoltaic system (microgrid) connected to a distribution grid. The main objective is to propose an original methodology for the design of smart "Nearly Zero Energy Buildings" (NZEB) and a subsequent solution for energy sustainability. This has led us to use HOMER, PV*SOL, Monte Carlo and DesignBuilder software which were chosen from the range of options that were and are available. Subsequently, a synthesis of the selected software solutions was carried out and a new model - the "Energy Model of a Building" (EMB) - was proposed in the process of integration with the energy performance of buildings, as an original optimization basis for designing smart buildings and smart areas, and even cities. The proposed EMB has been verified and evaluated within the experiment.

An Innovative Modelling Approach Based on Building Physics and Machine Learning for the Prediction of Indoor Thermal Comfort in an Office Building

In this paper, an innovative hybrid modelling technique based on machine learning and building dynamic simulation is presented for the prediction of indoor thermal comfort feedback from occupants in an office building in Le Bourget-du-Lac, Chambéry, France. The office was equipped with Internet of Things (IoT) environmental sensors. A calibrated building energy model was created for the building using optimisation tools. Thermal comfort was collected using a portable device. A machine learning (ML) model was trained using collected feedback, environmental data from IoT devices and synthetic datasets (virtual sensors) extracted from a physics-based model. A calibrated energy model was used in co-simulation with the predictive method to estimate comfort levels for the building. The results show the ability of the method to improve the prediction of occupant feedback when compared to traditional thermal comfort approaches of about 25%, the importance of information extracted from the physics-based model and the possibility of leveraging scenario evaluation capabilities of the dynamic simulation model for control purposes.

Selection of Energy Upgrades for Canadian Single-Detached Residential Households Based on Occupancy Profile

The use of energy efficient building systems can play a key role in reducing energy consumption and the adverse impacts of greenhouse gas (GHG) emission. The occupancy profile of residential dwellings has a notable influence on the effectiveness of selecting appropriate energy upgrade retrofits. Building simulation models can be integrated to determine the impact of independent occupancy profile in realizing a building’s carbon mitigation target. In this paper, the most desirable energy upgrade retrofits are suggested for three different occupancy profiles by considering important economic parameters, such as the initial investment, payback period and environmental parameter such as GHG emissions. The three occupancy profiles considered were a single adult, couple without children and couple with children. For this purpose, a calibrated energy model was developed for a single-detached family household in British Columbia, Canada, which was equipped with power sensors for monitoring the real time energy data. From the calibrated energy model, three different energy upgrade retrofits (solar, window, and wall/roof insulation) were modelled for the occupancy profiles chosen and the most suitable energy upgrades were suggested. The results show that solar panels contributed the most in energy cost reduction and upgraded windows had the least GHG emission. With suitable financial initiative, the combination of all the three energy upgrades can be the best option in terms of environment and economy.