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Introduction. As many other countries, France is facing the challenge of modelling its residential energy consumption (which is cccounting for more than 30% of the total energy consumption and is contributing for more than 16% of national CO2 emissions ). Modelling residential energy consumption is essential to be able to understand national energy problematic and predict future trends, thus to be prepared to adapt policies and legislation in order to meet energy efficiency requirements at global and European levels as, for instance, the recent adopted Loi Relative à la Transition Energetique (2015) ...

Summary Using this Shiny app, one can visualize oil prices (from 2000 to 2021) and events that may have had an impact on prices. The idea here is to overlay historical event data on top of a time series graph. The user can select a year and a type of source of event data to be displayed (Oil Timeline or Wikipedia). Oil prices were retrieved thanks to the U.S. Energy Information Administration API (EIA). ...

Introduction. In the first part of this article we managed to generate a sampling grid matching the contour of the surveyed area,starting from a data frame containing spatial metrics. This sampling grid will be implemented when building the krige function available with the gstat library. Part 2 - Kriging In a nutshell, Kriging is a geostatistical method which : measures spatial variability of a geographic data attribute. allows to predict or estimate the value at the location where the true value is unknown. ...