Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Dinh Thanh Viet, Vo Van Phuong, Minh Quan Duong and Quoc Tuan Tran
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Dinh Thanh Viet: University of Science and Technology, The University of Danang, 54 Nguyen Luong Bang St., Lien Chieu District, Danang 550000, Vietnam
Vo Van Phuong: Danang Power Company Ltd., 35 Phan Dinh Phung St., Danang 550000, Vietnam
Minh Quan Duong: University of Science and Technology, The University of Danang, 54 Nguyen Luong Bang St., Lien Chieu District, Danang 550000, Vietnam
Quoc Tuan Tran: Univ. Grenoble-Alpes, CEA-LITEN, INES, 50 avenue du Lac Léman, 73375 Le Bourget-du-Lac, France

Energies, 2020, vol. 13, issue 11, 1-22

Abstract: As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

Keywords: wind power forecasting; renewable energy; neural network; particle swarm optimization; genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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