A novel hybrid deep neural network model for short‐term electricity price forecasting

2020 ◽  
Author(s):  
Chiou‐Jye Huang ◽  
Yamin Shen ◽  
Yung‐Hsiang Chen ◽  
Hsin‐Chuan Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Electricity Price Forecasting

Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6514
Author(s):  
Min Yi ◽  
Wei Xie ◽  
Li Mo
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Electricity Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Short Term ◽  
Swarm Optimization ◽  
Electricity Price Forecasting ◽  
Bp Model

In the electricity market environment, the market clearing price has strong volatility, periodicity and randomness, which makes it more difficult to select the input features of artificial neural network forecasting. Although the traditional back propagation (BP) neural network has been applied early in electricity price forecasting, it has the problem of low forecasting accuracy. For this reason, this paper uses the maximum information coefficient and Pearson correlation analysis to determine the main factors affecting electricity price fluctuation as the input factors of the forecasting model. The improved particle swarm optimization algorithm, called simulated annealing particle swarm optimization (SAPSO), is used to optimize the BP neural network to establish the SAPSO-BP short-term electricity price forecasting model and the actual sample data are used to simulate and calculate. The results show that the SAPSO-BP price forecasting model has a high degree of fit and the average relative error and mean square error of the forecasting model are lower than those of the BP network model and PSO-BP model, as well as better than the PSO-BP model in terms of convergence speed and accuracy, which provides an effective method for improving the accuracy of short-term electricity price forecasting.

Short-Term Electricity Price Forecasting Based on MPSO-BP Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 1351-1355 ◽  
Author(s):  
Yu Dong ◽  
Qiang Yang ◽  
Wen Jun Yan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Electricity Market ◽  
Optimal Solution ◽  
Electricity Price ◽  
Convergence Time ◽  
Price Forecasting ◽  
Short Term ◽  
Electricity Price Forecasting ◽  
Improved Performance

In this paper, we exploited the short-term electricity price forecasting issue by introducing a global search mechanism based on the improved particle swarm optimization (MPSO) algorithm for the neural network training. The proposed MPSO algorithm is used for the initial weights and threshold of BP neural network in the process of optimization. We then proposed a novel short-term electricity price forecasting model based on MPSO-BP neural network. The paper provides a number of examples of bidding model of the California electricity market to forecasting market clear price using BP neural network trained by MPSO. Through the comparative study of the conventional BP neural network and the proposed MPSO-BP neural network, the proposed method demonstrates improved performance in finding the optimal solution with excellent convergence time for all the simulated scenarios.

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