This paper presents a systematic review to align current and future research in smart grids
(SG) and smart urban energy systems (SUES) and unify the diverse but fragmented …

The building sector accounts for 36% of the total global energy usage and 40% of
associated Carbon Dioxide emissions. Therefore, the forecasting of building energy …

Forecast combinations have flourished remarkably in the forecasting community and, in
recent years, have become part of mainstream forecasting research and activities …

Short-term photovoltaic (PV) power forecasting is essential for integrating renewable energy
sources into the grid as it provides accurate and timely information on the expected output of …

Public management is an effort to deliver public services, programmes, and projects that are
efficient, effective, and equitable for the community. The planning and decision-making …

Deep learning models have progressively advanced time series forecasting due to their
powerful capacity in capturing sequence dependence. Nevertheless, it is still challenging to …

Recent trends in the Machine Learning (ML) and in particular Deep Learning (DL) domains
have demonstrated that with the availability of massive amounts of time series, ML and DL …

The M5 competition follows the previous four M competitions, whose purpose is to learn from
empirical evidence how to improve forecasting performance and advance the theory and …

Deep learning based forecasting methods have become the methods of choice in many
applications of time series prediction or forecasting often outperforming other approaches …

Inspired by the successful use of deep learning in computer vision, in this paper we
introduce ForCNN, a novel deep learning method for univariate time series forecasting that …