Research on power load forecasting based on combined model of Markov and BP neural networks

2010 ◽  
Author(s):  
Dongxiao Niu ◽  
Hui Shi ◽  
Jianqing Li ◽  
Cong Xu
Keyword(s):  
Neural Networks ◽  
Load Forecasting ◽  
Bp Neural Networks ◽  
Combined Model ◽  
Power Load ◽  
Power Load Forecasting

Short-term electric power load forecasting using feedforward neural networks

2004 ◽  
Vol 21 (3) ◽  
pp. 157-167 ◽  
Author(s):  
Heidar A. Malki ◽  
Nicolaos B. Karayiannis ◽  
Mahesh Balasubramanian
Keyword(s):  
Neural Networks ◽  
Electric Power ◽  
Load Forecasting ◽  
Feedforward Neural Networks ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting ◽  
Electric Power Load

Short-Term Load Forecasting for Electric Power System Based on DB Wavelet and Regression BP Neural Networks

2013 ◽  
Vol 380-384 ◽  
pp. 3018-3021
Author(s):  
Kun Zhang ◽  
Yan Hui Wang
Keyword(s):  
Neural Networks ◽  
Electric Power ◽  
Dynamic Balance ◽  
Load Forecasting ◽  
Electric Power System ◽  
Wavelet Coefficients ◽  
Bp Neural Networks ◽  
Short Term ◽  
Power Load ◽  
Short Term Load Forecasting

In order to ensure the dynamic balance of power load and improve the accuracy of short-term load forecasting, this paper presents a method of short-term load forecasting for electric power based on DB wavelet and regression BP neural networks. In this method, we get the wavelet coefficients at different scales through series decomposing of wavelet decomposition to load sample, and each scale wavelet coefficients for threshold selection, and then trained adjusted wavelet coefficients by regression BP neural networks, reconstructed load sequence predicted date through inverse wavelet transform. Finally, the accuracy of this method is significantly higher than BP neural network by examples verification.

scholarly journals Power Load Forecasting Model Based on Grey Neural Network Regression Combination

2020 ◽  
Vol 213 ◽  
pp. 03006
Author(s):  
Guozhen Ma ◽  
Ning Pang ◽  
Zeya Zhang ◽  
Yongli Wang ◽  
Chen Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
High Accuracy ◽  
Actual Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Model Method ◽  
Model Based ◽  
Power Load ◽  
Power Load Forecasting

Due to the limitations of a single power load forecasting model, the power load forecasting cannot be performed well. In order to obtain a greater closeness to predict results with actual data, this paper presents the power load forecasting model based on gray neural network combined return to Guangzhou, 2010 - 2019 on actual data for example, the results show that: As used herein, the combined model method has high accuracy and strong use value.

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