arXiv subjects

Pierrick Bruneau

Publications and source records attributed to Pierrick Bruneau.

7 recordsLinked to original sources

arXiv2024

Forecasting Intraday Power Output by a Set of PV Systems using Recurrent Neural Networks and Physical Covariates

Accurate intraday forecasts of the power output by PhotoVoltaic (PV) systems are critical to improve the operation of energy distribution grids. We describe a neural autoregressive model that aims to perform such intraday forecasts. We build upon a physical, deterministic PV performance model, the output of which is used as covariates in the context of the neural model. In addition, our application data relates to a geographically distributed set of PV systems. We address all PV sites with a single neural model, which embeds the information about the PV site in specific covariates. We use a scale-free approach which relies on the explicit modeling of seasonal effects. Our proposal repurposes a model initially used in the retail sector and discloses a novel truncated Gaussian output distribution. An ablation study and a comparison to alternative architectures from the literature shows that the components in the best performing proposed model variant work synergistically to reach a skill score of 15.72% with respect to the physical model, used as a baseline.

cs.LG↗

arXiv2023

Astronomical Images Quality Assessment with Automated Machine Learning

Electronically Assisted Astronomy consists in capturing deep sky images with a digital camera coupled to a telescope to display views of celestial objects that would have been invisible through direct observation. This practice generates a large quantity of data, which may then be enhanced with dedicated image editing software after observation sessions. In this study, we show how Image Quality Assessment can be useful for automatically rating astronomical images, and we also develop a dedicated model by using Automated Machine Learning.

astro-ph.IM↗

arXiv2023

Landscape of High-performance Python to Develop Data Science and Machine Learning Applications

Python has become the prime language for application development in the Data Science and Machine Learning domains. However, data scientists are not necessarily experienced programmers. While Python lets them quickly implement their algorithms, when moving at scale, computation efficiency becomes inevitable. Thus, harnessing high-performance devices such as multicore processors and Graphical Processing Units (GPUs) to their potential is generally not trivial. The present narrative survey was thought as a reference document for such practitioners to help them make their way in the wealth of tools and techniques available for the Python language. Our document revolves around user scenarios, which are meant to cover most situations they may face. We believe that this document may also be of practical use to tool developers, who may use our work to identify potential lacks in existing tools and help them motivate their contributions.

cs.DC↗

arXiv2022

Cold Start Active Learning Strategies in the Context of Imbalanced Classification

We present novel active learning strategies dedicated to providing a solution to the cold start stage, i.e. initializing the classification of a large set of data with no attached labels. Moreover, proposed strategies are designed to handle an imbalanced context in which random selection is highly inefficient. Specifically, our active learning iterations address label scarcity and imbalance using element scores, combining information extracted from a clustering structure to a label propagation model. The strategy is illustrated by a case study on annotating Twitter content w.r.t. testimonies of a real flood event. We show that our method effectively copes with class imbalance, by boosting the recall of samples from the minority class.

cs.LG↗

arXiv2019

Heteroscedastic Gaussian Process Regression on the Alkenone over Sea Surface Temperatures

To restore the historical sea surface temperatures (SSTs) better, it is important to construct a good calibration model for the associated proxies. In this paper, we introduce a new model for alkenone (${\rm{U}}_{37}^{\rm{K}'}$) based on the heteroscedastic Gaussian process (GP) regression method. Our nonparametric approach not only deals with the variable pattern of noises over SSTs but also contains a Bayesian method of classifying potential outliers.

stat.AP↗

arXiv2019

Data-driven forecasting of solar irradiance

This paper describes a flexible approach to short term prediction of meteorological variables. In particular, we focus on the prediction of the solar irradiance one hour ahead, a task that has high practical value when optimizing solar energy resources. As Défi EGC 2018 provides us with time series data for multiple sensors (e.g. solar irradiance, temperature, hygrometry), recorded every minute for two years and 5 geographical sites from La Réunion island, we test the value of using recently observed data as input for prediction models, as well as the performance of models across sites. After describing our data cleaning and normalization process, we combine a variable selection step based on AutoRegressive Integrated Moving Average (ARIMA) models, to using general purpose regression techniques such as neural networks and regression trees.

cs.NE↗

arXiv2019

Extracting localized information from a Twitter corpus for flood prevention

In this paper, we discuss the collection of a corpus associated to tropical storm Harvey, as well as its analysis from both spatial and topical perspectives. From the spatial perspective, our goal here is to get a first estimation of the quality and precision of the geographical information featured in the collected corpus. From a topical perspective, we discuss the representation of Twitter posts, and strategies to process an initially unlabeled corpus of tweets.

cs.IR↗