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Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization

Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization

Pavan Kumar Singh, Nitin Singh, Richa Negi
Copyright: © 2021 |Volume: 15 |Issue: 2 |Pages: 28
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859833|DOI: 10.4018/IJCINI.20210401.oa9
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MLA

Singh, Pavan Kumar, et al. "Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization." IJCINI vol.15, no.2 2021: pp.111-138. http://doi.org/10.4018/IJCINI.20210401.oa9

APA

Singh, P. K., Singh, N., & Negi, R. (2021). Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(2), 111-138. http://doi.org/10.4018/IJCINI.20210401.oa9

Chicago

Singh, Pavan Kumar, Nitin Singh, and Richa Negi. "Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.2: 111-138. http://doi.org/10.4018/IJCINI.20210401.oa9

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Abstract

With the upsurge in restructuring of the power markets, wind power has become one of the key factors in power generation in the smart grids and gained momentum in the recent years. The accurate wind power forecasting is highly desirable for reduction of the reserve capability, enhancement in penetration of the wind power, stability and economic operation of the power system. The time series models are extensively used for the wind power forecasting. The model estimation in the ARIMA model is usually accomplished by maximizing the log likelihood function and it requires to be re-estimated for any change in input value. This degrades the performance of the ARIMA model. In the proposed work, the model estimation of the ARIMA model is done using latest evolutionary algorithm (i.e., dynamic particle swarm optimization [DPSO]). The use of DPSO algorithm eliminates the need for re-estimation of the model coefficients for any change in input value and moreover, it improves the performance of ARIMA model. The performance of proposed DPSO-ARIMA model has been compared to the existing models.