Write a PREreview

Hybrid Forecasting of ESDD and Electrical Load Using MMPF+GARA with Real Data

by Vasileios Bantidakis, Stylianos Pappas, Dimitrios Bogris, and Georgios Fotis

Posted: May 28, 2026
Server: Preprints.org
DOI: 10.20944/preprints202605.1925.v1

Forecasting is a process that must be accurate, reliable, and fast, if it is applied on a sort-term or on-line basis. It plays a significant role in decision-making, enabling the overcoming of economic and operational problems. Traditional methods are proven effective when applied to linear or stationary assumptions. However, recent challenges are non-linear, high-dimensional and include more noise, requiring more complex methods. In order to overcome such a complexity, a hybrid approach, combining the Multimodel Partitioning Filter (MMPF) and a Genetic Algorithm for Resource Allocation (GARA), will be presented. The method aims to refine the initial probabilities (weights) provided by the MMPF through an iterative, fitness-driven search for optimal weight values. The comparison will be made between the proposed method and one previously presented that combined MMPF with Support Vector Machines (SVM).

You can write a PREreview of Hybrid Forecasting of ESDD and Electrical Load Using MMPF+GARA with Real Data. A PREreview is a review of a preprint and can vary from a few sentences to a lengthy report, similar to a journal-organized peer-review report.

Before you start

We will ask you to log in with your ORCID iD. If you don’t have an iD, you can create one.