A GA-PSO Hybrid Algorithm Based Neural Network Modeling Technique for Short-term Wind Power Forecasting
DOI:
https://doi.org/10.13052/dgaej2156-3306.3342Keywords:
Wind; Generation Forecasting; Genetic Algorithm; Particle Swarm Optimization; Neural NetworksAbstract
This article proposes a hybrid neural network modeling technique
for forecasting of wind power generation based on an integrated algo -
rithm combining genetic algorithm (GA) and particle swarm optimi-
zation (PSO). The share of wind energy in electric power generation
keeps growing supported by favorable environmental policies aiming
at achieving low-emission targets. However, due to the intermittent and
uncertain nature of wind flow, integration of wind power into electric
power systems brings operational challenges to address. Accurate wind
power generation forecasting tools play a key role to address the chal -
lenges. A multi-layered feed-forward artificial neural network model
optimized by a combination of genetic algorithm and particle swarm
optimization algorithm is developed in this work for wind power gen -
eration forecasting. The proposed technique is tested based on practical
information obtained from Goldwind Smart Microgrid in Beijing. The
performance of the proposed method is superior to neural network
models optimized using GA and PSO separately, as well as the bench-
mark persistence approach.
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