International Journal of Power and Energy Research

Application and Comparison of Evolutionary Techniques for Forecasting the Hellenic Grid Electricity Load

Download PDF (1184.1 KB) PP. 139 - 149 Pub. Date: October 12, 2017

DOI: 10.22606/ijper.2017.13001

Author(s)

Stylianos. Sp. Pappas^*
Department of Electrical and Electronic Engineering Educators, ASPETE – School of Pedagogical and Technological Education, N. Heraklion, 141 21 Athens, Greece

Abstract

Electric load forecasting is a process that has to be both fast and reliable. An accurate method of load forecasting plays the most crucial role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and operational problems connected to electrical energy production and distribution. In this study real data is used and the performance of three different techniques for adaptive electric load forecasting is evaluated. The first method is a combination of the well-established multimodel partitioning filter (MMPF) implementing extended Kalman filters (EKF) with Support Vector Machines (SVM), the second is the adaptive MMPF Kalman filters (KF) model and the third one is an artificial three layer feed-forward neural network (ANN). The results indicate that all three methods are reliable, however the combination of MMPF and SVM provides a more accurate load forecasting and at the same time identifies successfully both normal periodic behavior and any unusual activity of the electric grid.

Keywords

Adaptive multimodel partitioning filter (MMPF), Support vector machines (SVM), artificial neural networks (ANN), forecasting, Kalman filters (KF), electricity demand load.

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