Forecasting complex time series is ubiquitous and vital in a range of applications but
challenging. Recent advances endeavor to achieve progress by incorporating various deep …

The coronavirus disease 2019 (COVID-19) pandemic has urged many governments in the
world to enforce a strict lockdown where all nonessential businesses are closed and citizens …

Load forecasting is crucial for multiple energy management tasks such as scheduling
generation capacity, planning supply and demand, and minimizing energy trade costs. Such …

We explore the application of kernel-based multi-task learning techniques to forecast the
demand of electricity measured on multiple lines of a distribution network. We show that …

While electricity demand forecasting literature has focused on large, industrial, and national
demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand …

Probabilistic wind power forecasting is an important input in the decision-making process in
future electric power grids with large penetrations of renewable generation. Traditional …

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 …

We address the issue of public or bank holidays in electricity load modeling and forecasting.
Special characteristics of public holidays such as their classification into fixed-date and …

In the past, studies in short‐term electrical load forecasting have been rather sceptical on the
use of meteorological covariates like temperature for short‐term forecasting purposes. The …

Building efficient and sustainable transportation systems is a key challenge for
accommodating the fast-increasing population living in cities. Lack of efficiency in …