The transport sector is a major contributor to greenhouse gas emissions ...
We focus on electricity load forecasting under three important specifici...
Uncertainty quantification of predictive models is crucial in decision-m...
In the context of smart grids and load balancing, daily peak load foreca...
The recent abundance of data on electricity consumption at different sca...
We present the winning strategy of an electricity demand forecasting
com...
Transfer learning, also referred as knowledge transfer, aims at reusing
...
The coronavirus disease 2019 (COVID-19) pandemic has urged many governme...
Probabilistic forecasting of electricity load curves is of fundamental
i...
Generalized additive models (GAMs) are flexible non-linear regression mo...
Future grid management systems will coordinate distributed production an...
In this article, we aim at improving the prediction of expert aggregatio...
We propose a contextual-bandit approach for demand side management by
of...
In the last two decades the growth of computational resources has made i...
Motivated by the reconstruction and the prediction of electricity
consum...
Motivated by electricity consumption metering, we extend existing nonneg...
We consider the setting of sequential prediction of arbitrary sequences ...