Raphaël Nedellec

• Researcher at Talend

Electricity load forecasting faces rising challenges due to the advent of innovating technologies such as smart grids, electric cars and renewable energy production. For distribution network managers, a good knowledge of the future electricity consumption stands as a central point for the reliability of the network and investment strategies. In thi...

We sum up the methodology of the team tololo for the Global Energy Forecasting Competition 2012: Load Forecasting. Our strategy consisted of a temporal multi-scale model that combines three components. The first component was a long term trend estimated by means of non-parametric smoothing. The second was a medium term component describing the sens...

We sum up the methodology of the team Tololo on the Global Energy Forecasting Competition 2014 for the electric load and electricity price forecasting tracks. During the competition, we used and tested many statistical and machine learning methods such as random forests, gradient boosting machines, or generalized additive models. In this paper, we...

Short-term electricity forecasting has been studied for years at EDF and different forecasting models were developed from various fields of statistics or machine learning (functional data analysis, time series, non-parametric regression). We are interested in the forecasting of energy data at different scales (national electricity load, substations...

The last decades improvements in processing abilities have quickly led to an increasing use of data analyses implying massive data-sets. To retrieve insightful information from any data driven approach, a pivotal aspect to ensure is good data quality. Manual correction of massive data-sets requires tremendous efforts, is prone to errors, and result...

Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R package. While the GAM methods provided by mgcv are based on the assumption that the response distribution is modelled parametrically, here we discuss more flexible methods that...

In the last two decades the growth of computational resources has made it possible to handle Generalized Additive Models (GAMs) that formerly were too costly for serious applications. However, the growth in model complexity has not been matched by improved visualisations for model development and results presentation. Motivated by an industrial app...

In the last two decades the growth of computational resources has made it possible to handle Generalized Additive Models (GAMs) that formerly were too costly for serious applications. However, the growth in model complexity has not been matched by improved visualisations for model development and results presentation. Motivated by an industrial app...

Quantile regression (QR) provides a flexible approach for modelling the impact of several covariates on the conditional distribution of the dependent variable, without making any parametric distributional assumption. Motivated by an electricity load forecasting application, we develop a computationally reliable framework for additive QR based on pe...

The connection of a distributed generation (DG) unit to a distribution network may lead to current or voltage constraints: generation curtailment can be a way to solve them. In this article, we explain how a DG curtailment volume can be estimated. This volume could be used in a contract between the distribution system operator (DSO) and the owner o...

While current research priorities include investigations of age-related hearing loss, there are concerns regarding effects on childhood hearing, for example through increased personal headphone use. By utilising historical data, it is possible to assess what factors may have increased hearing problems in children in the past, and this may be used t...