Short Term Load Forecasting: Individual Day Load Pattern using Artifical Neural Network for SLDC of Gujarat

2016 ◽  
Vol 3 (12) ◽  
pp. 1-6
Author(s):  
Tejas Gandhi ◽  
◽  
Sweta Shah
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Artifical Neural Network ◽  
Short Term Load Forecasting ◽  
Load Pattern

scholarly journals Layer Recurrent Neural Network based Power System Load Forecasting

2015 ◽  
Vol 16 (3) ◽  
pp. 423
Author(s):  
Nikita Mittal ◽  
Akash Saxena
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Load Forecasting ◽  
Absolute Error ◽  
Percentage Error ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Load Pattern ◽  
Facility Expansion ◽  
Large Distribution

This paper presents a straight forward application of Layer Recurrent Neural Network (LRNN) to predict the load of a large distribution network. Short term load forecasting provides important information about the system’s load pattern, which is a premier requirement in planning periodical operations and facility expansion. Approximation of data patterns for forecasting is not an easy task to perform. In past, various approaches have been applied for forecasting. In this work application of LRNN is explored. The results of proposed architecture are compared with other conventional topologies of neural networks on the basis of Root Mean Square of Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). It is observed that the results obtained from LRNN are comparatively more significant.

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

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