Research on electricity price forecasting method based on genetic algorithm and neural network in power market

2018 ◽  
Author(s):  
Yonggang Li ◽  
Ruizhi Liang ◽  
Zhanjun Li ◽  
Jing Gao ◽  
Youyin Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Power Market ◽  
Electricity Price ◽  
Price Forecasting ◽  
Electricity Price Forecasting ◽  
Forecasting Method

scholarly journals Electricity Price Forecasting using a Hybrid of Neural Network and Genetic Algorithm

2019 ◽  
Vol 8 (12S2) ◽  
pp. 675-679
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Hybrid Model ◽  
Electricity Price ◽  
Energy Market ◽  
Percentage Error ◽  
Price Forecasting ◽  
Electricity Price Forecasting ◽  
The Neural Network ◽  
The Individual

Electricity price forecasting has gained a reputation for its importance in the deregulated energy market. The forecast process can be complicated as it depends on many elements. This paper proposes a hybrid of a neural network with a genetic algorithm for the electricity price forecasting. The Ontario energy market is select as the tested market for this model. The features for the neural network input are the actual historical demand and actual Hourly Ontario Energy Price (HOEP). The genetic algorithms help to select the number of features and to optimize the parameters of the neural network. This hybrid model helps to improve the accuracy of the forecasted price when comparing with the accuracy of the individual neural network itself. The mean absolute percentage error has represented the accuracy of the hybrid model, and it is used as a benchmark of the proposed hybrid model with other models.

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