Search Results for author:
Found 9 papers, 4 papers with code
Matching of Everyday Power Supply and Demand with Dynamic Pricing: Problem Formalisation and Conceptual Analysis
At its core, this idea consists in providing the consumer with a price signal which is evolving over time, in order to influence its consumption.
From global to local MDI variable importances for random forests and when they are Shapley values
Random forests have been widely used for their ability to provide so-called importance measures, which give insight at a global (per dataset) level on the relevance of input variables to predict a certain output.
Importance measures derived from random forests: characterisation and extension
Their complexity is to such an extent that these models are commonly seen as black-boxes providing a prediction or a decision which can not be interpreted or justified.
A deep generative model for probabilistic energy forecasting in power systems: normalizing flows
This paper presents to the power systems forecasting practitioners a recent deep learning technique, the normalizing flows, to produce accurate scenario-based probabilistic forecasts that are crucial to face the new challenges in power systems applications.
A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market: extended version
This paper addresses the energy management of a grid-connected renewable generation plant coupled with a battery energy storage device in the capacity firming market, designed to promote renewable power generation facilities in small non-interconnected grids.
Random Subspace with Trees for Feature Selection Under Memory Constraints
Dealing with datasets of very high dimension is a major challenge in machine learning.
Context-dependent feature analysis with random forests
In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output.
Simple connectome inference from partial correlation statistics in calcium imaging
In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data.
Understanding variable importances in forests of randomized trees
Despite growing interest and practical use in various scientific areas, variable importances derived from tree-based ensemble methods are not well understood from a theoretical point of view.
