A comparison of artificial neural networks and support vector machines for short-term load forecasting using various load types

2017 ◽  
Author(s):  
Glen Mitchell ◽  
Sanjay Bahadoorsingh ◽  
Neil Ramsamooj ◽  
Chandrabhan Sharma
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Vector Machines ◽  
Short Term Load Forecasting ◽  
Artificial Neural ◽  
Load Types

scholarly journals Survey on short-term load forecasting using hybrid neural network techniques

2018 ◽  
Vol 7 (2.8) ◽  
pp. 464
Author(s):  
Shaive Dalela ◽  
Aditya Verma ◽  
A L.Amutha
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Load Forecasting ◽  
Theoretical Data ◽  
Electric Power System ◽  
Support Vector ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Artificial Neural

Load forecasting is an issue of great importance for the reliable operation of the electric power system grids. Various forecasting methodologies have been proposed in the international research bibliography, following different models and mathematical approaches. In the current work, several latest methodologies based on artificial neural networks along with other techniques have be discussed, in order to obtain short-term load forecasting. In this paper, approaches taken by different researchers considering different parameters in means of predicting the lease error has been shown.  The paper investigates the application of artificial neural networks (ANN) with fuzzy logic (FL), Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Support Vector Machines(SVM) as forecasting tools for predicting the load demand in short term category. The extracted outcomes indicate the effectiveness of the proposed method, reducing the relative error between real and theoretical data

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