The main and pivot part of electric companies is the load forecasting. Decision-makers and
think tank of power sectors should forecast the future need of electricity with large accuracy …

As renewable energy becomes increasingly popular in the global electric energy grid,
improving the accuracy of renewable energy forecasting is critical to power system planning …

We focus on solving the univariate times series point forecasting problem using deep
learning. We propose a deep neural architecture based on backward and forward residual …

Probabilistic forecasting, ie, estimating a time series' future probability distribution given its
past, is a key enabler for optimizing business processes. In retail businesses, for example …

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

This paper presents a deep residual network for improving time-series forecasting models,
indispensable to reliable and economical power grid operations, especially with high shares …

Predicting the dependencies between observations from multiple time series is critical for
applications such as anomaly detection, financial risk management, causal analysis, or …

Short-term electrical load forecasting plays an important role in the safety, stability, and
sustainability of the power production and scheduling process. An accurate prediction of …

Forecasting complex dynamical phenomena in settings where only partial knowledge of
their dynamics is available is a prevalent problem across various scientific fields. While …

Residential load forecasting is a challenging problem due to complex relations among the
hourly electrical load values along the time and also nonlinear relationships among the …