Short term load forecasting using a multilayer neural network with an adaptive learning algorithm

1992 ◽  
Vol 7 (1) ◽  
pp. 141-149 ◽  
Author(s):  
K.L. Ho ◽  
Y.-Y. Hsu ◽  
C.-C. Yang
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

scholarly journals Daily Peak Load Forecast Using Artificial Neural Network

2019 ◽  
Vol 9 (4) ◽  
pp. 2256
Author(s):  
Ramesh Kumar V ◽  
Pradipkumar Dixit
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Peak Load ◽  
Load Forecasting ◽  
Percentage Error ◽  
Ann Model ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Artificial Neural

The paper presents an Artificial Neural Network (ANN) model for short-term load forecasting of daily peak load. A multi-layered feed forward neural network with Levenberg-Marquardt learning algorithm is used because of its good generalizing property and robustness in prediction. The input to the network is in terms of historical daily peak load data and corresponding daily peak temperature data. The network is trained to predict the load requirement ahead. The effectiveness of the proposed ANN approach to the short-term load forecasting problems is demonstrated by practical data from the Bangalore Electricity Supply Company Limited (BESCOM). The comparison between the proposed and the conventional methods is made in terms of percentage error and it is found that the proposed ANN model gives more accurate predictions with optimal number of neurons in the hidden layer.

scholarly journals Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network

2016 ◽  
Author(s):  
Cheng-Ming Lee ◽  
Chia-Nan Ko
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Load Forecasting ◽  
Support Vector ◽  
Initial Structure ◽  
Short Term ◽  
Power Company ◽  
Learning Rates ◽  
Proposed Model ◽  
Short Term Load Forecasting

A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN). In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO) is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision as compared to various RBFNNs.

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