Open AccessElectricity Price Forecasting using a Hybrid of Neural Network and Genetic Algorithm
Author(s) -
Norazana Ibrahim,
Intan Azmira Wan Abdul Razak,
Zul Hasrizal Bohari
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l1117.10812s219
Subject(s) - electricity price forecasting , artificial neural network , benchmark (surveying) , genetic algorithm , electricity market , computer science , electricity , energy (signal processing) , process (computing) , artificial intelligence , machine learning , engineering , statistics , mathematics , geodesy , geography , electrical engineering , operating system
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.