Electricity Price Forecasting using a Hybrid of Neural Network and Genetic Algorithm
N.N.A.N. Ibrahim1, I.A.W.A. Razak2, Z.H. Bohari3

1N.N.A.N. Ibrahim, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Malacca, Malaysia.

2I.A.W.A. Razak, Industrial Power Department of Faculty of Electrical Engineering, Center for Robotics and Industrial Automation (CeRIA), Universiti Teknikal Malaysia Melaka (UTeM), Malacca, Malaysia.

3Z.H. Bohari, Faculty of Electrical and Electronic Engineering Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malacca, Malaysia.

Manuscript received on 10 December 2019 | Revised Manuscript received on 23 December 2019 | Manuscript Published on 31 December 2019 | PP: 675-679 | Volume-8 Issue-12S2 October 2019 | Retrieval Number: L111710812S219/2019©BEIESP | DOI: 10.35940/ijitee.L1117.10812S219

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.

Keywords: Electricity Price Forecasting, Genetic Algorithm Neural Network, Short-Term Forecasting.
Scope of the Article: Algorithm Engineering