Frédéric Suard

Extracting relevant features to explain electricity price variations

This paper proposes to explain the variations of energy price, namely the electricity on the German market. Such price variations are described by a set of characteristics which are not totally relevant to explain the variations. We first propose to find explanations by using visual tools in order to draw some preliminary conclusions. Analysing such kind of data is usually done thanks to visual comparison by plotting the curves chronologically. In a second time, we propose to build a statistical model from data. The aim of such approach is to detail the characteristic that get involved in the solution, so that we can automatically extract the most pertinent characteristics. We apply this approach on a set of historical data (2007–2010). Obtained results show that methodology is very interesting, since the conclusion from the statistical modelling enforce the visual analysis and also add details about the explanation.

Using Kernel Basis with Relevance Vector Machine for Feature Selection

This paper presents an application of multiple kernels like Kernel Basis to the Relevance Vector Machine algorithm. The framework of kernel machines has been a source of many works concerning the merge of various kernels to build the solution. Within these approaches, Kernel Basis is able to combine both local and global kernels. The interest of such approach resides in the ability to deal with a large kind of tasks in the field of model selection, for example the feature selection. We propose here an application of RVM-KB to a feature selection problem, for which all data are decomposed into a set of kernels so that all points of the learning set correspond to a single feature of one data. The final result is the selection of the main features through the relevance vectors selection.

Battery Capacity Estimation and Health Management of an Electric Vehicle Fleet

This paper addresses the challenge of monitoring battery in fleets of electric vehicles (EV). The goal is to identify the different EV utilization modes, during real use, and to highlight the constraining conditions to manage battery health evolution. The other part of this study aims to build a battery capacity estimation model, permitting to estimate the remaining capacity of an EV during its real use. Finally, is explained a strategy to improve capacity estimates, based on the identified EV utilization modes.