New compact semianalytical expressions of the scalar potential and magnetic fields produced by a radially polarized permanent magnet are described in this paper by using Coulombian approach. It uses fictitious magnetic charge to model the magnetic field intensity. With this fast model, we can compute the demagnetization field in each point inside the permanent magnet and the magnetic fields outside it

Purpose – This paper is a synthesis paper which seeks to discuss an optimisation framework using software components, which is a new emerging paradigm in computer science. Design/methodology/approach – The goal of this paper is to show the efficiency of the software component approach for the implementation of optimisation frameworks for engineering systems in general, and electromagnetic systems in particular. Findings – This paper highlights the component standard, a generator based on analytical expressions of the system, and an optimization service. References and examples show application in the area of electromagnetic components and systems. Practical implications – This paper presents CADES, a framework dedicated to system design, based on optimization needs. The framework is defined with a standard implementing the software component paradigm and a pattern to use it. Indeed, this pattern details how to create and use a component (the model of the device to design). Originality/value – This paper shows how the new emerging paradigm of software components can be used for building new generations of optimisation environment allowing capitalisation and reuse by combination of software components containing models and optimisation algorithms.

Buildings play a central role in energy transition, as they were responsible for 67.8% of the total consumption of electricity in France in 2017. Because of that, detecting anomalies (outliers) is crucial in order to identify both potential opportunities to reduce energy consumption and malfunctioning of the metering system. This work aims to compare the performance of several outlier detection methods, such as classical statistical methods (as boxplots) applied to the actual measurements and to the difference between the measurements and their predictions, in the task of detecting outliers in the power consumption data of a tertiary building located in France. The results show that the combination of a regression method, such as random forest, and the adjusted boxplot outlier detection method have promising potential in detecting this type of data quality problem in electricity consumption.

The big challenge for the 21th century is to decrease fossil energy use and to increase renewable energies in the framework of the climate constraint. The paper will show that smart buildings, connected to smart grids, can significantly contribute to this objective. Indeed, buildings are, on one side, the biggest consumers of energy in the electrical grid and could be among the greatest producers of renewable energy, especially thanks to the concept of energy positive buildings, and this by offering at the same time high flexibility in energy demand. That is why the paper focuses on methodologies using physical models and optimization for smart design and smart supervision for valorizing those buildings energy properties and contribute thus to the emergence of the concept of smart buildings (SBs) integrated in smart grids (SGs): we will give an overview of the mathematical optimization method used and of the kind of physical models we have developed over 10 years of active research in order to propose by this way a smart software dedicated to those SBs integrated in SGs. We explain also our global research strategy for improving this smart software, by a so-called “human-in-the-loop” approach, in which we consider that they there will be no “smart building” without “smart users”. This means a complex multi-disciplinary research that we develop in a “living lab”, in which the inhabitants are involved as “pro'sumers”, i.e. as active and implicated designers and users.