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Electrical Load Forecasting Between 2015 and 2035 for Turkey Using Mathematical Modeling and Dynamic Programming


Author(s):

Hayri O URLU , Turkish Electricity Transmission Company; Nurettin ÇET?NKAYA, Selcuk University

Keywords:

Electric Load Forecasting, Non Linear Regression, Particle Swarm Optimization, Artificial Neural Network, Ant Colony Optimization, Special Coefficients Dynamic Programming, Mathematical Model

Abstract:

This study, tried to explain the importance of planning studies about providing electrical energy to consumers with high quality, constantly and economic. The most important aspect of the planning, it is known that the load forecast. Up to now, many different load forecasting methods have been used. Some of these can be listed as Model for Analysis of Energy Demand (MAED), Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Nonlinear Regression (NLR) and Optimized Grey Method (OGM). In this article; proposed model for load forecasting was created using Mathematical Model (MM) and Special Coefficients Dynamic Programming (SCDP). The data obtained by these two models are compared with other models. Accordingly, using energy consumption data for the past year, MM and SCDP models are created and energy forecast is made for next year.


Other Details:

Manuscript Id :IJSTEV2I8100
Published in :Volume : 2, Issue : 8
Publication Date: 01/03/2016
Page(s): 279-283
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