Using matrix factorisation for the prediction of electrical quantities

The prediction task is attracting more and more attention among the power system community. Accurate predictions of electrical quantities up to a few hours ahead (e.g. renewable production, electrical load etc.) are for instance crucial for distribution system operators to operate their network in the presence of a high share of renewables, or for energy producers to maximise their profits by optimising their portfolio management. In the literature, statistical approaches are usually proposed to predict electrical quantities. In the present study, the authors present a novel method based on matrix factorisation. The authors' approach is inspired by the literature on data mining and knowledge discovery and the methodologies involved in recommender systems. The idea is to transpose the problem of predicting ratings in a recommender system to a problem of forecasting electrical quantities in a power system. Preliminary results on a real wind speed dataset tend to show that the matrix factorisation model provides similar results than auto regressive integrated models in terms of accuracy (MAE and RMSE). The authors' approach is nevertheless highly scalable and can deal with noisy data (e.g. missing data)

Gravimetric evidences of active faults and underground structure of the Cheliff seismogenic basin (Algeria)

International audienceThe Cheliff basin (ex El Asnam) is known as one of the most seismic active zone in Algeria and the West Mediterranean region. We can cite the El Asnam earthquake which occurred in 10.10.01980 with magnitude of 7.3. It was generated by a thrust fault with NE–SW sinistral component. Until now, there is a little information about existence of deep active faults, which generate this strong activity. The gravity field is an important resource of information on crustal structure. The aim of this work is giving a reliable geometry of the major faults relative to the kinematics of this region.The results obtained from various filtered maps (derivatives, upward continuation) of the gravity data, were used to generate a structural map of the studied area. Whilst the continuous wavelet transform method can help in automatic detection of elongated structures in 3-D, to estimate their strike direction, shape and depth. It gives a 3-D image or a model of the region and confirms the existence of several faults, localized or inferred, from former geological studies

Properties and customization of sensor materials for biomedical applications.

Low-power chemo- and biosensing devices capable of monitoring clinically important parameters in real time represent a great challenge in the analytical field as the issue of sensor calibration pertaining to keeping the response within an accurate calibration domain is particularly significant (1–4). Diagnostics, personal health, and related costs will also benefit from the introduction of sensors technology (5–7). In addition, with the introduction of Registration, Evaluation, Authorization, and Restriction of Chemical Substances (REACH) regulation, unraveling the cause–effect relationships in epidemiology studies will be of outmost importance to help establish reliable environmental policies aimed at protecting the health of individuals and communities (8–10). For instance, the effect of low concentration of toxic elements is seldom investigated as physicians do not have means to access the data (11)

Comparing time-series clustering approaches for individual electrical load patterns

This work positions the task of grouping electricity load time series among the vast field of clustering, and highlights corresponding research issues. A selection of the most performant time-series clustering approaches from the signal processing community are compared on the same dataset, composed by domestic electricity load profiles from Spain. The cross-correlation-based distance of Paparrizos and Gravano (2015) is shown to provide the best trade-off between clustering accuracy and CPU time

In situ studies of bovine serum albumin adsorption onto functionalized polystyrene latices monitored with a quartz crystal microbalance technique

International audienceThe adsorbability of bovine serum albumin (BSA) onto poly(styrene‐co‐itaconic acid) (PS–IA), poly(styrene‐co‐hydroxyethyl methacrylate) (PS–HEMA), poly(styrene‐co‐acrylic acid) (PS–AA), and poly(styrene‐co‐methacrylic acid) (PS–MAA) latices were investigated with a quartz crystal microbalance. The amount adsorbed onto the functionalized latices, except for PS–MAA, was greater than that adsorbed onto polystyrene (PS) latex. To explain this result, two kinds of interaction forces were considered, hydrogen bonding and hydrophobic interactions, whereas electrostatic interaction was assumed to be small. When comparing the two extremes of hydrophobic interaction and hydrogen bonding, the latter was stronger. The corrected adsorption mass suggested that the BSA molecules were adsorbed onto the PS–MAA latex in a side‐on mode. However, in the case of the PS, PS–IA, PS–HEMA, and PS–AA latices, the BSA molecules were probably adsorbed in multiple layers. The presence of the BSA in the latex particle surface was verified by attenuated total reflectance/Fourier transform infrared spectroscopy and atomic force microscopy