State-Space Models for Online Post-Covid Electricity Load Forecasting Competition

EUNITE Network Competition: Electricity Load Forecasting

Forecasting Daily Electricity Load Curves

Short term electricity load forecasting is a well-known problem, and many neural computing approaches for solving it have been proposed in recent years. In this paper, we argue in favour of its decomposition into two subproblems, and propose a solution for one of them: the prediction, en bloc, of the daily load profile, or configuration, for the different hours of a particular future date. From...

Optimized Clusters for Disaggregated Electricity Load Forecasting

• To account for the variation of EDF’s (the French electrical company) portfolio following the liberalization of the electrical market, it is essential to disaggregate the global load curve. The idea is to disaggregate the global signal in such a way that the sum of disaggregated forecasts significantly improves the prediction of the whole global signal. The strategy is to optimize, a prelimin...

A Practical Approach for Electricity Load Forecasting

This paper is a continuation of our daily energy peak load forecasting approach using our modified network which is part of the recurrent networks family and is called feed forward and feed back multi context artificial neural network (FFFB-MCANN). The inputs to the network were exogenous variables such as the previous and current change in the weather components, the previous and current statu...

Incremental Ensemble Learning for Electricity Load Forecasting

The efforts of the European Union (EU) in the energy supply domain aim to introduce intelligent grid management across the whole of the EU. The target intelligent grid is planned to contain 80% of all meters to be smart meters generating data every 15 minutes. Thus, the energy data of EU will grow rapidly in the very near future. Smart meters are successively installed in a phased roll-out, and...