Short-Term Power Load Forecasting Based on Empirical Mode Decomposition and Deep Neural Network

2019 ◽  
pp. 757-768
Author(s):  
Limin Cheng ◽  
Yuqing Bao
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Deep Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Mode Decomposition ◽  
Power Load ◽  
Power Load Forecasting

Empirical Mode Decomposition based Multi-objective Deep Belief Network for short-term power load forecasting

2020 ◽  
Vol 388 ◽  
pp. 110-123 ◽  
Author(s):  
Chaodong Fan ◽  
Changkun Ding ◽  
Jinhua Zheng ◽  
Leyi Xiao ◽  
Zhaoyang Ai
Keyword(s):  
Empirical Mode Decomposition ◽  
Load Forecasting ◽  
Deep Belief Network ◽  
Short Term ◽  
Belief Network ◽  
Multi Objective ◽  
Mode Decomposition ◽  
Power Load ◽  
Power Load Forecasting

A Short-Term Power Load Forecasting Method Based on BP Neural Network

2014 ◽  
Vol 494-495 ◽  
pp. 1647-1650 ◽  
Author(s):  
Ling Juan Li ◽  
Wen Huang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
High Speed ◽  
Load Forecasting ◽  
High Accuracy ◽  
Back Propagation Algorithm ◽  
Short Term ◽  
Power Load ◽  
Forecasting Method ◽  
Power Load Forecasting

Short-term power load forecasting is very important for the electric power market, and the forecasting method should have high accuracy and high speed. A three-layer BP neural network has the ability to approximate any N-dimensional continuous function with arbitrary precision. In this paper, a short-term power load forecasting method based on BP neural network is proposed. This method uses the three-layer neural network with single hidden layer as forecast model. In order to improve the training speed of BP neural network and the forecasting efficiency, this method firstly reduces the factors which affect load forecasting by using rough set theory, then takes the reduced data as input variables of the BP neural network model, and gets the forecast value by using back-propagation algorithm. The forecasting results with real data show that the proposed method has high accuracy and low complexity in short-term power load forecasting.

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