A novel machine learning-based electricity price forecasting model based on optimal model selection strategy

Energy ◽  
2022 ◽  
Vol 238 ◽  
pp. 121989
Author(s):  
Wendong Yang ◽  
Shaolong Sun ◽  
Yan Hao ◽  
Shouyang Wang
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Electricity Price ◽  
Selection Strategy ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Optimal Model ◽  
Model Based ◽  
Electricity Price Forecasting

A Comparison of Deep Learning vs Traditional Machine Learning for Electricity Price Forecasting

2021 ◽  
Author(s):  
Christian O'Leary ◽  
Conor Lynch ◽  
Rose Bain ◽  
Gary Smith ◽  
Diarmuid Grimes
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Electricity Price ◽  
Price Forecasting ◽  
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.

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